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Conveying Robot Navigation
Intention to Humans
Alyxander David May
MAY11213081
Computer Science, MComp
The University of Lincoln
April 2015
Alyxander David May
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Conveying Robot Navigation
Intention to Humans
Alyxander David May
Alyxander David May
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Acknowledgments
First and foremost I would like to thank my supervisor Dr Marc Hanheide,
even before the onset of this project he showed the faith in me that I needed to push
through. Without his invaluable support in robotics and more so the correct research
methodologies to follow surely this project couldn’t have been achieved to this level.
Without Christian Dondrup’s support and in depth knowledge of aspects
ROS, Python and much more I wouldn’t have been able to meet my deadlines.
Always approachable and able to offer help when needed his expertise were of vital
importance to me.
Matthew Ashton for providing me with all I needed to create a functional
workspace to continue on my work through to the small hours of the morning.
A thanks to all the members of the STRANDS team for all the hard work they
have done in developing the Scitos G5, to get it to the current state and allow this
project to be run using one of their robots.
Lincoln Centre for Autonomous Systems and Professor Tom Duckett for
allowing me to use their office and workspaces for the lifecycle of this project.
Finally, a thanks to all my fellow peers for pushing me through this project;
special thanks to all those who participated in the experiment.
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Abstract
The general aim is to gauge and measure in terms of comfort felt and ease of
understanding, how human-robot interaction can be enhanced by implementing and
empirically evaluating methods of expressing navigational intent on a mobile robot.
Three behaviours were developed for displaying navigational intent: No Signal,
Indicators and Move Head. These behaviours were then evaluated by designing and
running an experiment to empirically evaluate them. The robot used was a Scitos
G5 with a Human Machine Interface and Indicators attached.
The experiment ran over the course of three days with ten participants, who each
filled out a survey attempting to ascertain: how easy it was to understand the intent
of the robot, how quickly they could understand the robot and how comfortable they
felt around the robot. Of the behaviours Indicators was the most preferred in each
aspect, with a statistically significant different mean difference compared to No
Signal, and a higher average score on each aspect than Move Head. The minimum
distance kept from the robot was also analysed, with participants keeping a statically
significant further distance when the robot was using Indicators compared to No
Signal, and an average further distance compared to Move Head.
Participants were also asked to choose which of the behaviours they would most
like to see implemented as a standard for social robotics, four selected Move Head
a believed it felt most natural all with self-confess little or no knowledge of robotics.
While six selected Indicators, as they believed it is the most obvious, most with some
or more robotics knowledge.
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Table of Contents
Acknowledgments ....................................................................................... iii
Abstract .......................................................................................................iv
List of Tables & Figures.............................................................................. vii
1 Introduction ............................................................................................ 1
1.1 Background ..................................................................................... 1
1.2 Aims & Objectives ........................................................................... 2
1.2.1 Aim............................................................................................. 2
1.2.2 Objectives .................................................................................. 3
1.3 Rationale ......................................................................................... 3
2 Literature Review ................................................................................... 4
2.1 Human-Robot Interaction ................................................................ 4
2.2 Human-Robot Spatial Interaction .................................................... 6
2.3 Robot Navigational Signals ............................................................. 8
3 Design.................................................................................................... 9
3.1 Project Management ....................................................................... 9
3.1.1 Traditional.................................................................................. 9
3.1.2 Supervision & Deliverables ...................................................... 11
3.1.3 GitHub...................................................................................... 12
3.2 Research Methodology.................................................................. 12
4 Software Development......................................................................... 16
4.1 Tools & Machine Environments..................................................... 16
4.1.1 Robot Operating System.......................................................... 16
4.1.2 Ubuntu ..................................................................................... 16
4.1.3 Python...................................................................................... 16
4.1.4 Robots ..................................................................................... 17
4.1.5 SCITOS G5.............................................................................. 19
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4.2 Development Methodology............................................................ 20
4.2.1 Incremental Development........................................................ 20
4.2.2 Action Lib................................................................................. 21
4.2.3 Testing..................................................................................... 22
5 Experiment........................................................................................... 23
5.1 Design ........................................................................................... 23
5.1.1 Physical ................................................................................... 23
5.1.2 Robot Setup............................................................................. 24
5.1.3 Participants .............................................................................. 25
5.2 Results .......................................................................................... 26
5.3 Evaluation...................................................................................... 28
5.3.1 Questionnaire Sections 1, 2 & 3............................................... 28
5.3.2 Questionnaire Section 4........................................................... 33
5.3.3 Robot Data............................................................................... 35
5.3.4 Summary ................................................................................. 37
5.4 Limitations ..................................................................................... 38
5.5 Future Work................................................................................... 40
5.6 Conclusion..................................................................................... 42
6 Personal Reflection.............................................................................. 43
7 Bibliography ......................................................................................... 45
8 Appendices .......................................................................................... 51
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List of Tables & Figures
Figure 1 - Traditional Project Management (Project Lifecycle Services Ltd,
2014)...................................................................................................................... 9
Figure 2 - Empirical Research Cycle (Explorable, 2015) ........................... 13
Figure 3 – Rovio (Robotdom, 2015)........................................................... 17
Figure 4 – Turtlebot (Turtlebot, 2015)........................................................ 18
Figure 5 – inMoov (3diot, 2014)................................................................. 18
Figure 6 - Pioneer 3 – AT (Unmanned Vechicle Centre, 2015) ................. 18
Figure 7 – Scitos G5 (Lincoln Centre for Autonomous Systems, 2015)..... 19
Figure 8 - Robot Comparison Matrix.......................................................... 19
Figure 9 - Waterfall vs Incremental (Bittner, 2006) .................................... 20
Figure 10 - Client-Server Interaction (ROS, 2015)..................................... 21
Figure 11 - ROS Topic Graph.................................................................... 22
Figure 12 – Move Head Behaviour: left, Straight, Right............................. 24
Figure 13 – Indicate Behaviour: Left, Straight, Right ................................. 24
Figure 14 - Available Robot Paths ............................................................. 25
Figure 15 - Survey Results ........................................................................ 27
Figure 16 - Robot Area of Interest for Minimum Distances........................ 27
Figure 17 - Minimum Distance from Robot (m).......................................... 28
Figure 18 - Results Question 1 .................................................................. 28
Figure 19 - Score Differences for Question 1 ............................................ 29
Figure 20 - Results Question 2 .................................................................. 30
Figure 21 - Score Differences for Question 2 ............................................ 31
Figure 22 - Results Question 3 .................................................................. 32
Figure 23 - Score Differences for Question 3 ............................................ 33
Figure 24 - Average Minimum Distances................................................... 35
Figure 25 - Behaviour Distribution ............................................................. 36
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1 Introduction
1.1 Background
Robots are becoming increasingly more common in industrial environments
“…up to 2008 about 63,500 service robots for professional use were sold during a
period of more than 12 years. However, during the past five years some 100,000,
service robots for professional use were sold according to the results of these
statistics.” (International Federation of Robotics, 2014). Alongside this it is also
believed that robotics for the elder and disabled will increase with a forecast of
12,400 units to be sold in 2014-2017, “This market is expected to increase
substantially within the next 20 years.” (VDMA, 2013), with this increase in robots it
becomes more important to look at Human-Robot Interaction (HRI).
A key principle of mobile robotics is to achieving safe operation in the
presence of humans (Steinfeld, et al., 2006). Robots currently are successfully able
to navigate through environments including circumnavigating obstacles. However,
humans must be treated differently to ensure they feel safe and comfortable around
robots. “To improve the robot behaviour, we conducted a human-human experiment
to find a socially plausible strategy to behave in such situations.” (Kruse, et al.,
2012). A study conducted by Kruse, looked at how people perceived each other’s
movements in a path crossing scenario and what they preferred. This is where the
need for Human-Robot Spatial Interaction (HRSI) has come in, not only do robots
need to be aware of humans, but they need to be traversed in a way they are
comfortable with. By implementing a direct navigational intention signal from a robot,
humans will have a better understanding of what the robot intents; which leads to
better perception and comfort with robots. A study carried out by Breazeal et al,
identified that humans overall had a more positive interaction with a mobile robot
which game them non-verbal signals, i.e. a head nod or an eye blink, compared to
that of no signal (Breazeal, et al., 2005).
With the advance in robotics, we increasingly use robots to fulfil tasks we as
humans consider too dangerous, fatuous or inherently tedious. One area this can
be seen is in the care industry, where HRSI is paramount for safety of people either
ill, disabled or elderly. The STRANDS project, an EU commissioned research
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project is currently delving into this environment “STRANDS will produce intelligent
mobile robots that are able to run for months in dynamic human environments.”
( Automation and Control Institute, 2015). One of the partners for this project is Haus
der Barmherzigkeit (translates to ‘House of Mercy’) an Austrian elderly care home
in Vienna. Clients of Haus der Barmherzigkeit may suffer from various cognitive
and/or motoric disabilities, this may mean they have to impaired movement and/or
judgment (Haus der Barmherzigkeit, 2014). Therefore, a smooth easy to understand
action from a mobile robot in their environment is imperative to their comfort; more
so as it will be their own home. Another part of the STRANDS project is looking into
mobile security working alongside security guards “This is the first time that an
autonomous robot has been deployed in a working office environment to do a real
job.” (G4S, 2014). Again, in this environment it is imperative the robot is aware of
humans and how to interact and navigate around them, with other humans also
competing tasks alongside them.
1.2 Aims & Objectives
1.2.1 Aim
The general aim is to gauge and measure in terms of comfort felt and ease
of understanding, how human-robot interaction can be enhanced by implementing
and empirically evaluating methods of expressing navigational intent on a mobile
robot. Three different methods of expressing navigation intention will be
implemented and empirically reviewed. With these implementations it is hoped
improvements can be made in how humans perceive the navigational intent of
mobile robots through more legible signalling, as well as giving humans more
confidence working around or with mobile robots as they become part of everyday
life. By implementing various techniques of non-verbal communication, hypotheses
surrounding each of these implementations will be formed.
An experiment will be used to ascertain the research data. By applying
quantitative and qualitative research methods hypotheses will be tested and
evaluated, to see if there appears to be any significant differences between the
implementations.
Some of the key aspects to look at are: ‘Ease of understanding the
behaviour’, ‘Speed of understanding the behaviour’, ‘Was the subject comfortable
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around the behaviour’, etc. As can be seen from the questions, the main aspect is
to ascertain participant’s views on the various behaviours and is a very person
centred approach, with the participant being the key factor and not how the robot
responds.
1.2.2 Objectives
1) Continue literature review to identify three methods of expressing
navigational intent that are legible, safe and effective, as well as formation of
hypotheses for later testing
2) Implement chosen methods using ROS in Python and testing for
reliability using either a robot or a simulation environment
3) Create and run an experiment to test human perception for each
implementation, with appropriate post-study survey for data gathering
4) Use appropriate quantitative and qualitative methods to review data
acquired from the experiment and test against hypotheses
1.3 Rationale
The study is inherently a research project in that the key principle includes
divulging into an area which is currently strongly researched; HRI and more
specifically HRSI. With being a research project it is important to understand that
the key components are the results attained during the experiment and the
reflections they have on the implemented system, rather than the system as a
whole.
The hope of the project is to answer questions about how signals can improve
the comfort felt by humans around mobile robots. The work will look to answer
questions that have been purposed by Dondrup et al. by looking at the psychology
behind using different signals to achieve more comfortable navigational intent
(Dondrup, et al., 2015). Principles will be used including signals previously
implemented and discussed by Peters et al., who have left scope for their research
to be continued alongside Dondrup et al.’s with signals being implemented from both
(Peters, et al., 2011). This gap in research can be seen in a recent survey of Human
aware navigation by cruse et al, the finds much in the way of robots navigating to
and around humans and the comfort attached with this, navigational intent signals
is not addressed (Kruse, et al., 2013).
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2 Literature Review
One of the biggest issues to remember whilst conducting any research with
robots is that they have limitations and you have to work within these limitations. As
described by Dudek and Jenkin “It is interesting to note that fictional robots usually
do not suffer from the computational, sensing, power, or locomotive problems that
plague real robots.” (Dudek & Jenkin, 2010). Although, this work shouldn’t be
resource hungry, it is still important aspect to remember in any robot related
research, it might not be perfect due to various limitations.
On the other hand it is also important to understand the scope of current
robotics. They range from replacing the aging workforce in the automobile
manufacturing industry (Byrant, 2014) to helping interpretation and the transport of
food to people in West Africa aiding the Ebola outbreak (Gaudin, 2014) and helping
in with bomb disarmament and disposal in war zones (Defence, 2014) and many
more in between. For the purpose of this project a mobile personal robot will be
used.
2.1 Human-Robot Interaction
Within social robotics, some people are more experienced in interacting with
robots than others, this can lead to possible positive and negative connotations for
research, but also biased. A study conducted by Hall et al., concluded that
“Participants who self-reported greater robotics knowledge reported higher overall
engagement and greater success at developing a relationship with the robot.” (Hall,
et al., 2014). This study shows the importance of ascertaining people’s robotics
experience in social robotics research. This work wishes to look at the comfort felt
by participants (amongst other aspects), thus it is important during the experiment
to ascertain the robotics experience of participants.
Human perception is constantly changing for robots, this can lead to a more
natural and comfortable interaction for humans. Hansen et al., show this change in
perception by researching into gesture recognition with mobile robots, specifically
they attempt create a robot system that analyses a participants posture and interact
with them accordingly (Tranberg Hansen, et al., 2009). The system shows clear
machine learning with the ability to adapt to different human behaviours over time.
A similar study by Svenstrup et al. uses a similar approach to gauge human interest
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in an interaction; using a three variable Case Based Reasoning system similar to
that used Tranberg Hansen et al. (Svenstrup, et al., 2009). Both of these studies
show examples of a robot learning to perceive humans in different scenarios to
understand their needs. This work intends to look at the inverse; looking at how
humans perceive the robot and understating the needs and intention of the robot in
the form of navigational intent.
Robot signalling is an area increasingly research, this can be seen in the
work of Mutlu et al., who investigate the psychological effects of a robot using gaze
cues for a turn taking conversation in a group (Mutlu, et al., 2009). The study
concludes those in the conversational groups “…also felt more acknowledged,
welcomed, and valued by their group, and that they belonged more to the group…”
Similar work has been done by Bennewitz et al.; who implemented an informative
humanoid robot to give information to patrons at a museum (Bennewitz, et al.,
2005). The experiments found that “Almost all people found the eye-gazes,
gestures, and the facial expression human-like and felt that Alpha was aware of
them.” (Alpha was the humanoid robot used in the study). Both of these show
humans feel more involved in a conversation situation when receiving signals or
gazes from a robot. It’s the hope of this work to see if these concepts can be
transposed to navigational signals; to increase the comfort felt for humans by
receiving navigational signals. Another point raised by these studies is eye gaze
feeling natural, as such; eye gaze will be one of the signals used in this study to
display navigational intent.
A study at Massachusetts Institute of Technology by Breazeal et al.,
conducted an experiment in which participants were tasked to teach a robot the
location of buttons and to press the corresponding button (Breazeal, et al., 2005).
The control group received no feedback from the robot, whereas the other test group
received a visual facial expression, a nod for understanding and a confused face for
non-understanding. “H1: Subjects are better able to understand the robot’s current
state and abilities in the IMP+EXP case”, this hypothesis states the authors believe
participants will be able to better understand the robot when it expresses
understanding using facial expression. From the results it was concluded “There
was a significant difference between the two manipulators on answers to questions
about subject’s ability to understand the robot’s current state and abilities. Thus
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Hypothesis 1 is confirmed…” The study shows that humans are better suited to
understand the intention of a robot when it is display signals to them. In this work, it
is intended to follow on using these principles to see if they apply to other aspects
of HRI specifically Spatial Interaction and whether signals improve the
understanding navigational intent.
2.2 Human-Robot Spatial Interaction
Torta et al., conducted an experiment in which a human sat with a humanoid
robot approaching from various angles and distances to initiate communication
(Torta, et al., 2011). The study showed that the optimal approach angle for
participant comfort was directly in front rather than from the side. A similar study, by
Walters et al., use standing participants and a mobile robot (Walters, et al., 2011).
The effects looked at here are long term comfort rather than short term, results
showed that participants felt more comfortable approaching the robot in a confined
space compared to being approach by the robot in the same confined space. This
work will continue on the themes shown in these studies, the principle will be taken
from Torta et al., to use a head on encounter, similar to a corridor passing behaviour,
whilst also allowing the participants to move in and around the robot in whatever
way feels comfortable to them; the work attempts to build on what has been
previously shown by trying to investigate in which scenarios humans feel more
comfortable around mobile robots.
The distance, speed and direction a robot moves in relation to a human is
important in achieving the most comfortable HRSI. Work by Pacchierotti et al., looks
into the feelings of humans at varying distances and speeds in a hallway setting
(Pacchierotti, et al., 2005). The results show that “The best behaviour was,
according to all the subjects, behaviour 8 (see Table III), i.e. the one with highest
speed and largest signaling and lateral distances”. Although the scope of this work
doesn’t include looking into varying speeds and distances, it is an important point to
note in that participants’ feelings may be greatly affected by the speed and or
distance the robot takes in in avoidance path. For this reason, it will be established
that the same speed, angle and path will be taken by each different behaviour, to
ensure consistency.
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What kind of behaviour do humans expect from moving robots? One aspect
of this question, a path crossing scenario was investigate by a Lichtenthaler et al.
they attempted to look at how humans wanted a robot to react in a direct path
crossing situation and to test various implementations they created an experiment
(Lichtenthäler, et al., 2013). The authors concluded that the ideal situation is where
the robot the heads directly towards the goal and only changes its path by halting if
it were to invade the personal space of a crossing human. This was also the scenario
in which the participants felt most comfortable around. A similar study by Dondrup
et al., looked at whether hesitation signals could be seen in as path crossing
scenario (Dondrup, et al., 2014). The results show “that hesitation signals can be
found in head-on encounters during pass-by scenarios”, these could lead to
increased stress and less comfortable feelings around robots. In both of these
experiments, the concept of navigation intent signals could have made a difference
to how comfortable and confident participants felt around the robots. This work will
attempt to look at the comfort felt during the pass by scenario and evaluate any
differences between various forms of navigational intent.
One way being investigated of improving the comfort felt by humans around
social robots it to look at robots exhibiting more “natural” behaviour. Saulnier et al.,
look at using robot body language to catch people’s attention. In their study a robot
approach a group of two people and exhibited one of three behaviours: ignore and
pass by, approach with quick and erratic behaviour or approach slowly. The
participants confirmed they felt navigational behaviour conveyed messages from the
robot (Saulnier, et al., 2011). Althuas et al. conducted a study in which a robot
approached a group of people, by approaching the middle of the group body facing
toward the group. The subjects felt the behaviour exhibited by the robot felt natural
(Althaus, et al., 2004). Finally a study by Satake and Hayashi conducted
experiments with of robots approaching humans in a shopping centre (Satake, et
al., 2009) & (Hayashi, et al., 2011). They found people felt it was most comfortable
for the robot to approach from the front. It is important to remember the principle of
“The Uncanny Valley” in which Mori describes the phenomenon of robots becoming
too human-like can cause discomfort (Mori, et al., 2012). These studies show that
humans do feel more conferrable around robots conforming to natural human
behaviour; to an extent. One of these natural behaviour is to look where we are
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going, this can be implemented on a mobile robot to “look” in the direction it intends
to move as a form of navigational intent.
2.3 Robot Navigational Signals
Peters et al., performed a study based around a pass by scenario in a
hallway, participants were asked how they felt about the manner in which a mobile
robot should express navigational intent (Peters, et al., 2011). The navigational
signals used in the experiment were: sideways motion, forward or backwards
motion, stopping, screen signals or camera motion. The results showed that
forwards, backwards, and sideways motion was were favourable with a combine
49% of people preferring them, compared to 22% for screen signals and less than
10% each for the rest. A question in the survey asked users if there was another
way they would like to see navigational intent expressed, 60% of people responded
with indicators. Indicators are a simple but effective and well known medium for
expressing navigational intent that most people will be aware of from transport, it
would be implemented onto a robot for the study.
The second experiment, in a study conducted by Dondrup et al., attempts to
look at the way in which humans interact with a robot in a corridor passing scenario
(Dondrup, et al., 2015). Two participants were involved, both human however, one
was dressed in a robot costume with their eyes and face hidden. The “robot”
received instructions on movement and collision avoidance via a set of headphones.
The other participant was naïve to the end goal of the experiments, they were just
instructed to cross the corridor without colliding with the “robot” and with as little
movement as possible. The robot had a tablet positioned on its chest after the start
of each trial could either display eyes, looking right or left, indicators again right or
left or no signal. At no point during the study did they attempt to deceive the
participant i.e. indicate left and then move right. The robot would also change when
it would signal from either 1s, 1.5s or 2s into the test. The purpose of the study was
to investigate different aspects of HRSI using Qualitative Trajectory Calculus state
chains, however the psychology aspects of the difference in human interaction
during the second experiment weren’t looked at. The authors stated “Some of the
more interesting phenomena in the experiments, especially the “Bristol Experiment”,
like if the indicators had an effect on the interaction between the two agents or if the
timing was important for the use of the indicators, will be investigated in more
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psychology focused work.” (Bristol Experiment refers to the experiment spoken
about in this paragraph, the second experiment of the work) The experiment for this
work will attempt to follow the same strategy of the second experiment, keeping as
many factors the same as possible, however for the purpose of this work; only the
various signals will be looked at not the timings as well thus just having one variable.
Two of the behaviours present in this study will be used, the indicators and the eyes.
However, the will be physically implemented rather than using a screen.
3 Design
3.1 Project Management
3.1.1 Traditional
This project will use the traditional project management approach. As the
project is a research project, with a coding, experiment and evaluation it seems
appropriate to use a rigid methodology. As the project is neither client nor user
driven, an agile methodology isn’t needed, as the requirements are unlikely to
change in any significant manner until the entire process is completed. The stages
can be broken down into: Aim & Objectives (Business Requirements), System
Requirements, Design, Development (Build), Experimental Analysis (Test) and
Evaluation (Deploy). The cycles as seen below in Figure 1 is a waterfall style, with
one aspect being completed before the next is started.
Figure 1 - Traditional Project Management (Project Lifecycle Services Ltd, 2014)
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Aims and Objectives has already been completed in the project proposal and
are listed previously in the report.
System Requirements follows, in which the literature review is a major
aspect. The review shows what research has been conducted and the gaps in it,
thus hinting at research that needs to be continued. The system requirements for
this project will be bases around an extension on the aims and objectives and gaps
in the literature review. The system requirements are as follows:
1) Implement a way of using indicators to signal navigational intent
2) Implement a way of using the head to signal navigational intent
3) Create a randomized testing strategy capable of handling a non-
specified amount of subject
Design is the next aspect, this will not just include software design; but also
the design of the experiment and the methodologies used in the later evaluation of
the project. The software will be designed around the system requirements, using
established frameworks. The software for this project however is minimal, there is
greater interest in the experiment than the actual software. Thus the design for the
experiment is pivotal. The experiment design will attempt to remove as much bias
as possible as well as running in an ethical, professional and appropriate manner;
by doing this it should allow greater reliability of results attained. Finally, the
appropriate paperwork for the subjects in the experiments must be design, this will
include: consent form, demographic form and post experiment survey. The forms
must include all relevant information as well as being informative and ethically
correct by informing participants of their rights.
Building again is not entirely software based, as the experiment is such a
vital part. The first aspect of the build will be the software development, this allows
a working prototype in either a simulation or a real world situation; giving the basis
for the experiment setup. In addition to the software, additional hardware will need
to be added and mounted to the robot; 5V indicators will be added to allow the robot
to indicate, as well as a rotating head. Additional changes to the software will be
made after the addition of the indicators and head. Following on from the software
and hardware the experiment will be designed, as well as minor changes to the
software to incorporate the location of the study. The experiment will be in a single
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self-contained and controlled environment, using the same robot to allow for the
greater accuracy of results. The final stage will be to record data from the robot of
the position of the subject in the experiment.
Experimental Analysis is one of the most important aspect of the research.
All attempts will be made to remove bias from the experiment to ensure a high
quality as possible data set. The experiment will occur in Lincoln Centre for
Autonomous Systems research laboratory over the course of a few days.
Participants will be handled on an individual basis to stop participants who have
experience the experiment sharing their opinions with those that haven’t. This
section also includes coagulating the data formulated in to the experiments to a
format that can later be reviewed; this includes the survey questions and the
positions of the participants from the robot. All the data will be tabulated and
appropriate graphs and paired t-tests etc. will be used to create the final results.
Evaluation is the last element, and starts by discussing and concluding on
the results attained. A key part of the evaluation is to look at the limitations of the
project and ways it could be improved if undertaken again. Most importantly, the
evaluation should preclude to possible extensions that could be looked at with the
knowledge attained from this iteration. Finally a critical reflection in which the
aspects of the project management methodology will be critiqued and reflected upon
as to whether they were appropriate in the context of this project, as well as what
could have been done differently from a project management perspective.
3.1.2 Supervision & Deliverables
To ensure that the project continues as expected and time frames are being
met, regular supervision with the project supervisor will take place. Meetings will
take place on at least a fortnightly basis, thus giving time for decent strides to be
taken, but at the same time not long enough to fall majorly behind the goals. As well
as regular meetings, non-formal contact will be used if any problems arise within the
project that doesn’t require face-to-face time; this will be done using email
predominately and could be for any small matters. However, should a matter arise
that does require extra contact time, additional meetings can be planned on pro re
nata basis.
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At various times during the project deliverables will be needed rather than
just communication, a summary of Goals and Time Frames can be found along with
a Gantt chart in Appendix items D and E respectively. The main deliverables will be:
software, evidence of functional software, experiment plan and results. The
software will be hosted on a code revision site that the supervisor will have public
access too, thus can be reviewed at any time. The software will be run in a simulator
environment that can be either viewed at a meeting or a video can be sent to the
supervisor; evidence of functionality will be needed before testing on a real robot.
The experiment plan will be delivered with a physical showcase, including an
explanation of why it has been prepared in the manner shown, and a copy of all the
relevant paperwork that will be used in the course of the experiment. Results will be
shown after the experiment and in the form of graphs and charts thus easier to
understand and digest than raw data. Finally, all documents will be available for the
supervisor to view on a file hosting service, meaning easy feedback can be given
in-between meetings. As well as this a risk assessment and contingency plan list
has been created and can be seen in Appendix item F.
3.1.3 GitHub
GitHub is a Git repository host service. Git hub allows easy source code
management (SCM), code revision, collaboration and code access worldwide (Git
Hub, 2015). From a project management perspective, a code revision system is
invaluable, it will ensure that the code is always kept on the cloud and accessible
anywhere. As well as this, it allows easy access for code to be tested on different
machines and most importantly the robot itself. Finally, it allows easy support for the
project supervisor, they are able to quickly look at the code and what work has been
done in-between meetings, comments can be left and they can trial the code before
using it on a real robot.
3.2 Research Methodology
During the course of this project, the empirical research approach will be
used, empirical research with its distinctive sections ties in well with tradition project
management. “Empirical research is based on observed and measured phenomena
and derives knowledge from actual experience rather than from theory or belief.”
(Cahoy, 2015), empirical research stems from the philosophical medium Empiricism
which is described as gaining knowledge through experience (Duignan, 2015).
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Empirical research has been shown to work very well with social robotics and with
HRSI, examples of this can be seen in the work of: Hansen et al., Peters et al.,
Saulnier et a., and more mentioned in the literature review (Tranberg Hansen, et al.,
2009) & (Peters, et al., 2011) & (Saulnier, et al., 2011).
One of the main factors of the empirical research methodology specifically in
HRSI is a direct interaction experiment, for the purpose of this work it will be in a
laboratory environment. This methodology has been proven in many situations such
as work by Young et al., in which they evaluated a dog lead system for robots, with
the robot either walking in front or following the participant (Young, et al., 2011).
Other examples already spoken about in the literature review include: Dondrup et
al. and the robot signalling experiment (Dondrup, et al., 2015), Pacchierotti et al.
and the robot approach experiment (Pacchierotti, et al., 2005).
Empirical research contains the following steps:
1) Observation
Observe by collecting and analysing empirical facts
2) Induction
Formulating hypotheses via Induction
3) Deduction
Deduct consequences with attained empirical data
4) Testing
Test hypotheses with new empirical data
5) Evaluation
Evaluate the outcome of testing
Figure 2 - Empirical Research Cycle (Explorable, 2015)
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Observation for this research has been covered by doing an in depth
literature review of projects surrounding the area of navigational intent and the use
of signals from mobile robots. Various projects have been studied and analysed,
each with a slightly different view point on the research goal, thus giving a deep yet
broad underpinning to the aims of this project. The literature review has shown that,
humans do much prefer a signal of navigational intent from a robot in various
scenarios, as well there is a maximum and minimum “natural” distance and speed
a robot should use when moving around humans. What hasn’t been addressed in
much detail however, is the specifics of how different signals alter the perception,
understanding and comfort for humans regarding robot navigation intent, this is the
main area of concern for the project, to attempt to distinguish between various
signals.
Induction is using what has been seen in the literature review and forming
specific hypotheses from the general view. To form the hypotheses, general
principles seen in the review must be taken. The generalized principle from the
review is that humans prefer a signal of navigational intent. Thus, hypotheses can
be drawn from these principles.
The hypotheses drawn from the literature review are:
1) Humans feel more comfortable and are able to quickly and correctly
understand the intention of a robot when using indicators to express
navigational intention compared to that of no signal
2) Humans feel more comfortable and are able to quickly and correctly
understand the intention of a robot when using its head to express
navigational intention compared to that of signal
3) Humans will move further away from the robot when it is indicating
navigational intent than when using no signal
4) Humans will move further away from the robot when it is using its head
to express navigational intent than when using no signal
Using the general principle and induction, it has previously been shown that
humans prefer navigational signals from a mobile robot. Therefore, humans would
probably prefer the robot to use indicators and a head movement to no signal, as
both of these are signals of navigational intent.
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Deduction is where aspects relevant to the study are conceptualize and the
formation of the experiment begins. Humans are exposed to indicators on a regular
basis when either on the road in a vehicle or walking. Humans understand that when
a vehicle indicates left, it intends soon to turn left. From this we can deduce the
same principle and apply it to the robot, by applying indicators we can assume that
when a robot indicates left it intends to turn to the left and vice versa. The same can
be deduced regarding human navigation, humans generally look in the direction
they are walking and intent to move, this can also be applied to robotics. We can
apply a head movement to the robot moving its head and eyes to “look” at the
direction it intends to travel, this should be deducted by the human as the robot
looking in the direction it intends to travel.
Testing will be completed by an experiment. Participants will complete a set
of trails, interacting with the robot in a controlled environment. All efforts will be made
to reduce the bias in the experiment to as low as possible. As well as this it will be
attempted to get a diverse test data set, specific aspects that will try to be attained
are a spread of ages, gender and knowledge of robotics. Furthermore, to attempt to
lower any bias the order of the test will be randomized for each participant with only
the controller know what will happen during the experiments. The data obtained in
the experiments will be quantitative and qualitatively examined, then tested against
the hypotheses formulated in the induction. Data will be attained as a mixture of
software capture from the robot and opinions of the participants in the form of a
survey.
Evaluation starts by looking at the graphs and charts formulated by the data
collected in the experiment, these will created as using means standard error of
means, and mode; including other statistically accepted formulas. As participants
will complete all of the seven tests, a paired t-test will be used to see if there is a
statistically significant difference between the three behaviours. The hypotheses will
be critiqued as well as the testing strategy. The evaluation should reference back to
the literature review and discuss if findings collaborate with the research of the
previous authors, as well as highlight the differences between the works. There will
be a discussion into what the possible connotations of the findings (if any) are as
well as looking at possible improvements if a second iteration to take place; finally
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looking into the possible extensions of the project and further research that could be
undertaken as a direct extension to the project.
4 Software Development
4.1 Tools & Machine Environments
4.1.1 Robot Operating System
ROS is used for a variety of tasks: HERE maps (Delaney, 2014), Aerospace
Research (Foote, 2013), Industrial Manipulators (Intermodalics, 2015), amongst
others. Carrying out a research project, with real world connotations, it is imperative
to attempt to use established mediums to ensure reliability, it is clear to see that
ROS is not only a proven system but an adaptable one with more than enough
functionality for this project.
“ROS (Robot Operating System) provides libraries and tools to help software
developers create robot applications. It provides hardware abstraction, device
drivers, libraries, visualizers, message-passing, package management, and more.”
(Willow Garage, 2015). ROS is a peer-to-peer robot middleware package, it allows
easier hardware extraction and code reuse. All major functionality is extracted into
separate nodes, which typically run in a separate process. Communication
4.1.2 Ubuntu
The main ROS client libraries are created in accordance with Unix-like
systems, predominately due to the open source nature of the software
dependencies (ROS, 2015). In light of this the only supported operating system is v
various Ubuntu versions dependant on the version of ROS. It is important to note
that while ROS is available for Windows and Mac, they not supported and thus are
maintained by the community, this is one of the key reasons that Ubuntu was the
chosen Operating System for the development of the software. Ubuntu is also the
preferred and supported OS for the industrial version of ROS; ROS-Industrial (ROS-
I), “Like ROS, ROS-I nominally runs on Ubuntu Linux…” (ROS Industrial, 2015).
4.1.3 Python
The coding language used for the purpose of the experiment was python.
With ROS only two languages are available, C++ and Python. Due to the nature of
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this experiment, the coding aspect is of minimal importance and the advanced
functionality of C++ isn’t required. Although Python will take time to learn; is an
interpreted language and thus doesn’t require to be complied each and every time
it is run and tested, this will time will make up for the added time to learn. Alongside
this, it terms of ease of understanding Python is far simpler as it more similar to
simple English. All resources need for the project are available in Python, as well as
a large set of tutorials and how to help on wiki ROS and other such similar websites.
4.1.4 Robots
The university have various different appropriate robot systems available that
could be used for this project, those considered for the project are: Rovio, Turtlebot,
Pioneer 3-AT, MARC (based on inMoov robot) and Scitos G5. The robot required,
will need to be mobile, capable of using indicators and a head movement to display
navigational intent. The robot should also pose enough of a physical obstacle so
that participants are required to circumnavigate the robot and not just walk over it.
Only robots with a camera have been looked at, this is due to needing a way for the
robot to track the position of the subject during the tests.
Rovio is a small mobile webcam, with a speaker,
microphone and articulating head capable of three different
positions, and is roughly shin height. While it is mobile, there
is no easy way to attach the desired indicators to the robot.
Another added issue although the head can move; it only
moves in the vertical axis, which wouldn’t be an intuitive way
to express navigational intent. Thus this would have to be explained to participants
before the experiment and giving away vital information about the nature of the
experiment. Finally, the robot is far too small in stature, it could easily be walked
over, for all these reasons it won’t be used (WowWee, 2015).
Figure 3 – Rovio
(Robotdom, 2015)
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Turtlebot is an open source and hardware robot. Due to
the nature of turtlebot, it is highly customizable. The University
has turtlebots powered by an i5 Intel Nuc, the Nuc is sufficient
to add the capabilities of indicators to the robot. Additional
hardware in the form of an articulating head would however
need to be added to the robot to make it suitable for the tasks.
With turtlebot only being approximately knee, it is another
robot that could be walked over instead of circumnavigated.
The robot has a Kinect on board that would be able to track
people in 3D space. Due to the additional hardware and size this robot wouldn’t be
appropriate to use for the experiment.
MARC (Multi-Actuated Robotic Companion) is a 3D
printed robot based on the open source inMoov project by
Gael Langevin (Langevin, 2015). MARC has a fully
articulated head, fingers and arms, with speaker, a
microphone, chest mounted Kinect camera, two cameras in
the eyes and is capable of people tracking in 3D space.
Being the size of a small person, MARC is not something that
can be walked over. MARC is capable of moving the head
with 120o of freedom and being open hardware based on a
Pololu electronics, indicators could easily be added. The
limiting factor is currently MARC is not mobile as the legs are still being designed by
Gael. A possible solution to this could be to place MARC on wheels, however this
isn’t a very elegant design and could subtract from the end perspective of the
participants, being very unnatural looking.
Pioneer 3-AT is a highly versatile four wheeled robotic platform.
Similar to the turtlebot, it is highly customizable and able to add
additional hardware. Out of the box the platform doesn’t contain
the required hardware, a 3D camera similar to the Kinect,
indicators and a articulated head would have to be added,
although this is possible, it is rather a lot of hardware work.
Included with this, the robot base is only shin height meaning to
add a head to it, substantial fabrication would be needed to
Figure 5 – inMoov (3diot,
2014)
Figure 4 – Turtlebot
(Turtlebot, 2015)
Figure 6 - Pioneer 3 –
AT (Unmanned
Vechicle Centre, 2015)
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make adding an articulated head to the system. So although the system is rugged
and highly customizable, it is too much additional work for the purpose of a project
like this.
Scitos G5 is a robot with a full working computer and touch
screen. Including additional hardware G5 stands at the height of a
small person; meaning participants would have to circumnavigate.
Included in the additional hardware is a head unit in which the eyes
are capable of full 360o rotation. Various voltage ports are also
available to power indicators. The robot also has two mounted 3D
cameras capable of tracking participants in 3D space. It’s because of
these reasons, along with needing little additional hardware work that
the G5 has been chosen.
The key aspects for each robot can be seen in Figure 8.
Rovio Turtlebot MARC Pioneer 3-AT Scitos G5
Mobile ✔ ✔ X ✔ ✔
3D Camera X ✔ ✔ X ✔
Rotating Head Up and Down X ✔ X ✔
Ports for Indicators X ✔ ✔ ✔ ✔
Large Presence X X ✔ X ✔
Figure 8 - Robot Comparison Matrix
4.1.5 SCITOS G5
Scitos G5 (aka Linda) robot with a Human Machine Interface enclosure
manufactured by Metralabs Germany: Scitos G5 is about 1.5m (ca. 5ft) tall and
weighs about 75kg (ca. 165Lbs.). The Scitos G5 conforms to the European CE-
guidelines for the public indoor sector and was certified by the German Technical
Inspection Agency (TÜV). The Scitos G5 does not have any end-effectors and is
therefore only able to interact with humans via spatial movement, eye gaze, speech
and a touch screen (MetraLabs, 2015).
Due to other software running on the robot created by the STRANDS team,
it isn’t possible for the robot to actively collide with the human, it will stop, turn around
and attempt to find a new path if the human gets to close; for the purpose of this
experiment, this functionality will more than likely not be used. However it does
Figure 7 –
Scitos G5
(Lincoln Centre
for Autonomous
Systems, 2015)
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improve the safety aspect of the experiment. The controller of the experiment also
has an overriding controller for the robot, so that if unexpected or bizarre behaviour
occurs it can be remotely controlled and moved.
4.2 Development Methodology
4.2.1 Incremental Development
The software development, has three main aspects indicate, move head and
no signal. Thus it makes sense to deal with these individually, by developing one,
testing and refining it before moving on to the next aspect. A good way to deal with
this is using incremental development.
Figure 9 - Waterfall vs Incremental (Bittner, 2006)
Figure 9 shows an overview of the waterfall and iterative process, waterfall is
described as ‘Taking an extreme waterfall approach means that you complete a
number of phases in a strictly ordered sequence: requirements analysis, design,
implementation/integration, and then testing.’ By Kroll of IBM (Kroll, 2004). The
strictly ordered sequence is useful to the needs of this project, however agile
principles of recurring development would also be useful. Using the incremental
development style, the waterfall process can be followed for each of the different
behaviours, i.e. design, code, integrate and test no signal, before completing the
same process for indicators and then move head.
By implementing this methodology, it gives the rigidness of waterfall to
ensure that each behaviours has been implemented correctly and tested with
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enough time before the experiment. However, by using incremental principles, it
allows the requirements to change, i.e. if it is found during the development phase
that there may be an issue implementing one of the three current behaviours, a new
behaviour could be designed and implemented in time for the experiment and thus
not allowing the time frames to be too drastically altered.
4.2.2 Action Lib
The action lib stack, will be the main way in which the system will publish new
goals to move base using ROS. Two scripts are required as can be seen in Figure
10, one to act as the client that sends the goals and the other a server that deals
with the publishing of the goals.
Figure 10 - Client-Server Interaction (ROS, 2015)
Before any scripts are created, the action, feedback and results messages
for the server must be made, all of these will be Pose Stamped messages,
containing a status, map, position and orientation for the robot etc.
The server will be responsible for sending goals and receiving feedback from
move base and will publish results back to the client. Once the server receives the
goal, the goal will be published to move base, the server will then wait for move
base to be within 70cm of the published goal (using the Euclidean distance d(a, b)
= √(a1 – b1)2 + (a2 – b2)2 ), or if the goal has been completely reached. Once the
threshold has been met, the client will be informed and the current position of the
robot be sent back.
The client is where most of the functionality takes places. The first aspect is
to check what number participant the client last dealt with, found by opening a CSV
files containing the number of the last participant. Following this, the relevant CSV
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file for the participant is opened, containing the list of behaviours the robot will exhibit
in a randomized order. Each behaviour will have a related method in the client file,
this will contain all of the movement commands, and signal commands as well as a
wait at the end of behaviour, and this allows the participant to be back in place before
the next behaviour starts, which is controlled by the experiment controller.
Figure 11 - ROS Topic Graph
Figure 11 shows the ROS Topics used by the implemented code.
move_server first receives a goal from move_client then publishes it to move_base.
move_base then waits for a result from move_base before sending the result back
to move_client. move_server can also give feedback to move_client as to where the
robot is. move_client can also cancel the goal using move_server/cancel. Finally the
orientation is set by move_client using the DWAPlannerROS/parameter_update
dynamic reconfigure.
The indicators are powered by two of 5V ports on the robots PCB, to blink
the indicators the ports need to be reconfigured on the fly to power on and off. This
is done using the dynamic reconfigure (ROS, 2015) client, which allows a node’s
parameter to be changed without having to restart the node. Dynamic reconfigure is
also used to stop the robot attempting to set perfect orientation when it is moving
between the goals, by setting the orientation tolerance to 2π (360o). Finally, the rate
at 2Hz is controlled on a separate thread in which a sleep of 2Hz is applied before
turning on and off the indicator respectively. The second behaviour, move head is
controlled by sending a joint state message to topic head command, this allows a
pan and tilt value to be sent and reposition the head into its respective position.
4.2.3 Testing
Testing was completed using two different mediums, 3D simulation
environment Blender and the robot. By having a simulated testing environment, it
ensure that any small changes are able to be tested quickly and easily, also useful
for when the robot was unavailable. Some testing will take place on a real robot
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close to the time to the user study to ensure it is working as expect on real hardware
as well as simulated. All the testing completed was regarded as black box testing,
due to the software having nothing in the way of inputs, the test was simply to test
and ensure functionality of the robot was safe and got to the correct places.
5 Experiment
The main aspect of the experiment is to explore how various navigational
signals impact the way a human responds both physically and mentally to an
oncoming robot in a corridor passing scenario. The first aspect is to design and
implementation of a suitable and feasible experiment. Following this conclusions will
be drawn from the results of the experiment, as well as a discussion about how the
experiment was executed.
5.1 Design
5.1.1 Physical
The experiment was set up in Lincoln Centre for Autonomous Systems (L-
CAS), part of the University of Lincoln. The robot was position at one end of the
room 8 meters away from the participant facing one another, for each test the start
locations were the same.
Participants were asked to cross the room and arrive at the start location of
the robot with in a manner they felt comfortable with, however with the caveat that
they must start walking directly towards the robot. At the same time the robot would
move in the opposing direction, thus the robot and participant were on a head-on
collision path. Each participant completed seven different test, each with a different
behaviour. 4/7 test included signals of navigational intent (see Figure 12 and Figure
13 for signals).
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5.1.2 Robot Setup
The robot could display three signals of navigational intention: no signal,
indicate and move head. No signal, involved the robot moving between the points
without any visual signals. For indicate, 5V LED amber indicators installed (Figure
13) on either side of the “body” just below the “head”, when the indicators were
active, they would flash at a rate of 2Hz. Finally, move head involved moving the
“head” 35o to the left or right dependant on the behaviour needed (Figure 12). The
robot started each behaviour on a keyboard input, for the straight behaviour, the
robot moved 7.5m forward, and this is the only test in which the human would
actively have to circumnavigate the robot without a “collision”. For all other
behaviours the robot started by moving 1.5m forward before starting to signal, with
Figure 13 – Indicate Behaviour: Left, Straight, Right
Figure 12 – Move Head Behaviour: left, Straight, Right
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the exceptions of no signal, then a further 1m forward, followed by a 1.8m diagonal
movement (1m sideways and 1.5m forward), the signal was “reset” after this. Next
the robot moved 3m forward before reaching its terminal destination. This means
the robot could take three different paths, regardless of signal with the same start
position but different termination positions (Figure 14 shows available robot paths).
In total there are seven independent behaviours the robot can use and each
will be used on each participant in a randomized order. The behaviours are: move
left, move right, move straight, move left with head, move right with head, move left
with indicator and move right with indicator. It is important to note that when the
robot exhibited the various behaviours the robot indicated its own navigational intent
direction and it was not a command to the human. As well as this the robot did not
deceive the human in any of the behaviours i.e. it never indicated left and then
moved right.
5.1.3 Participants
Ten participants completed the experiment, although none had any
experience or idea of the nature of the experiment beforehand, half of the
participants had previous contact that day with the robot by participating in a
separate experiment. Each participant was issued with a general demographics
form and a consent form to read through and fill out before starting the experiment
(items A and B in appendices). Alongside the forms, the participants were verbally
informed that they had the right to withdraw at any point including after the
experiment and all data will be removed, and that all of their data was given
anonymously and couldn’t be traced back to them. The participants were also
informed they could ask questions before or after the experiment but not during as
to keep the integrity of the experiment.
Figure 14 - Available Robot Paths
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At the start of the experiment each participant was informed they were to
stand in the starting position and wait for the controller to instruct them to move.
Once they were instructed the test had begun, they were to walk casually towards
the robot and to navigate to the start point of the robot in as casual and natural
manner as possible. The participants were also told that although the robot would
not collide with them, its behaviour was not reactive to how they acted.
After the participant had completed all seven of the behaviours they were
issued with a survey form to fill in about their experience with the robot (item C in
appendices). The experiment was then explained, including what the purpose of the
research was, why it was being done, and what it was hoped the experiment would
show. Finally, any questions they had could be asked and answered before thanking
them for their time and reminding them of their rights.
5.2 Results
With ten participants completing the experiment, seventy unique data sets
were attained. The results from the survey are tabulated according to behaviour
exhibited and question, these can be seen below in Figure 15. The behaviours are
No Signal is NS, Indicator is I and Move Head is MH. The questions asked were:
1) “I was able to understand the intention of the robot when using this
behaviour.”
2) “I felt comfortable passing the robot while it was exhibiting this behaviour.”
3) “I was quickly able to understand the intention of the robot.”
Questions were answered using a Likert Scale: 1 - Strongly Disagree, 2 -
Disagree, 3 - Neutral, 4 - Agree and 5 - Strongly Agree.
Participant NS
1
NS
2
NS
3
I 1 I 2 I 3 MH
1
MH
2
MH3
1 2 3 2 4 4 3 4 4 4
2 2 2 2 4 4 5 4 3 4
3 1 2 4 5 4 5 3 2 4
4 2 3 1 5 4 5 4 4 4
5 2 2 3 4 2 4 4 5 4
6 3 4 2 5 5 5 4 4 3
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7 3 4 3 4 4 4 2 4 3
8 4 3 4 5 4 5 2 3 1
9 3 2 4 4 4 4 3 3 3
10 1 1 1 4 5 4 5 5 5
Figure 15 - Survey Results
As well as this, the robot tracked the position of the person, in 3D space,
using itself as the origin. For the interest of this experiment only the X and Y axis
are of need as they remained on the same height. The exact distance from the robot
will be used, using simple Pythagoras 𝑎2
+ 𝑏2
= 𝑐2
. The area of interest will be using
a field of view from 1
3
4
to
1
4
π, with π directly behind the robot and 0 directly in front,
the area of interest can be seen in Figure 16.
Figure 16 - Robot Area of Interest for Minimum Distances
Figure 17 shows minimum distances kept each test for each participant, unit
used is meters.
Participant Straight Left Right Indicate Left Indicate Right Head Left Head Right
P1 0.72647 0.75379 1.29989 1.62642 1.00610 1.16366 1.15255
P2 1.54390 1.50569 1.53723 1.61397 1.59526 1.19771 0.73154
P3 0.67134 0.57304 0.90999 1.68446 1.31759 0.69555 0.52814
P4 0.72808 0.64711 0.85181 1.35578 1.34077 1.02913 1.14908
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P5 2.41629 1.60650 1.33606 2.59011 2.44219 0.65425 1.13071
P6 1.11463 1.05780 1.29263 1.31898 2.17899 1.52792 2.16402
P7 1.25423 1.31454 1.42037 1.77986 1.92819 0.65792 1.07275
P8 0.61908 0.48311 0.48145 1.74640 1.04203 1.24667 0.78165
P9 0.74319 0.52994 0.33715 0.85010 0.86872 1.50603 1.70050
P10 0.47898 0.72195 0.99406 1.03909 0.99406 1.65893 1.06612
Mean 1.02962 0.91935 1.04606 1.56052 1.47139 1.13378 1.14771
Figure 17 - Minimum Distance from Robot (m)
5.3 Evaluation
5.3.1 Questionnaire Sections 1, 2 & 3
The first aspect of the evaluation is to look at the results of the surveys, the
results to the question 1, 2 and 3 for each behaviour can be seen below in Figures
18, 20 and 22, including standard error of mean error bars and the results of paired
t-test between No Signal and Indicators.
Figure 18 - Results Question 1
The first question looks at how well the participants could understand what
the robot was intending to do in each of the behaviours. Just by looking at the
graphs, indicators were most understandable, with the highest mean and lowest
standard error of mean, with the mean being 4.33 ± 0.17 and mode of 4. The second
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most understood was the move head movement behaviour, with a mean of 3.44 ±
0.27 and mode of 4. Finally the least understood behaviour was no signal, with a
mean of 2.22 ± 0.28 and mode of 2.
 There was extreme significant difference in the scores for Indicators
(M = 4.33, SD = 0.52) and No Signal (M = 2.22, SD = 0.95) with
conditions; t (9) = 6.68, p = >0.0001
 There was not quite a statistical difference in the scores for Move
Head (M = 3.44, SD = 0.97) and No Signal (M = 2.22, SD = 0.95) with
conditions; t (9) = 2.17, p = 0.0584
 There was a statistical significant difference in the scores for Indicators
(M = 4.33, SD = 0.52) and Move Head (M = 3.44, SD = 0.97); with
conditions t (9) = 2.38, p = 0.0414
Figure 19 - Score Differences for Question 1
These results suggest that indicators do affect humans understanding the
navigational intent of a robot compared to no signal and head movement this can
be seen in Figure 19 showing the score difference between no signal and indicators
for each participant. However there was there was no significant affect on the
understanding between head movement and no signal. Specifically, these results
suggest that robots using indicators to display navigational intent are more
understood by humans than those using a head movement or no singal, and robots
0
1
2
3
4
5
NO S IGNA L INDIC A T ORS
PARTICIPANT SCORES DIFFERENCE FOR
QUESTION 1
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using a head movement to display navigational intent are not significantly more
understood that those using no signal.
Figure 20 - Results Question 2
The second questions looks at how comfortable the participants felt when
passing the robot in each of the behaviours. Again participants selected indicators
as the most comfortable behaviour exhibited with the lowest standard error of the
mean; at mean 3.89 ± 0.24 mode of 4. Followed by closely by move head behaviour,
with a mean of 3.67 ± 0.28 mode of 4. Finally, no signal is lowest of the behaviours,
with mean 2.44 ± 0.35 mode of 2.
 There was a very statistically significant difference in the scores for
Indicators (M = 3.89, SD = 0.82) and No Signal (M = 2.44, SD = 0.97)
with the conditions; t (9) = 3.77, p = 0.0044
 There was no statistical significance in the scores for Indicators (M =
3.89, SD = 0.82) and Move Head (M = 3.67, SD = 0.95) with the
conditions; t (9) = 0.71, p = 0.4961
 There was a statistical significance in the scores for Move Head (M
= 3.67, SD = 0.95) and No Signal (M = 2.44, SD = 0.97) with the
conditions; t (9) = 2.54, p = 0.0318
Alyxander David May
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Figure 21 - Score Differences for Question 2
These results suggest that indicators and head movement do affect humans
comfort when passing a robot compared to using no signal this can be seen in Figure
21 showing the score difference between no signal and indicators for each
participant. However there was no statistical significance in the affect for using
indicator compared to head movement. Specifically, these results suggest that when
a robot is display navigation intent with either indicators of a head movement
humans are feel more comfortable passing the robot then not displaying any signal,
also humans do not feel significantly more comfortable passing a robot using
indicates instead of head movement.
0
1
2
3
4
5
NO S IGNA L INDIC A T ORS
PARTICIPANT SCORES DIFFERENCE
FOR QUESTION 2
Alyxander David May
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Figure 22 - Results Question 3
The third question looks at how quickly people could understand the intention
of the robot, participants also selected indicators as the quickest to understand the
intention of the robot; with a mean of 4.33 ± 0.23 mode of 5. Second again was
move head with a mean of 3.56 ± 0.34 mode of 4 and finally no signal with a mean
of 2.67 ± 0.35 mode of 2.
 There was a very statistically significant difference in the scores
between Indicators (M = 4.33, SD = 0.70) and No Signal (M = 3.56,
SD = 1.17) with the conditions; t (9) = 4.32, p = 0.0019
 There was not quite a statically significant difference in the scores
between Indicators (M = 4.33, SD = 0.70) and Move Head (M = 3.56,
SD = 1.08) with the condition; t (9) = 1.96, p = 0.0811
 There was not a statistically significant difference in the scores
between Move Head (M = 3.56, SD = 1.08) and No Signal (M = 3.56,
SD = 1.17) with the condition; t (9) = 1.41, p = 0.1934
Alyxander David May
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Figure 23 - Score Differences for Question 3
These results suggest that indicators do affect how quickly humans can
understand the navigational intent of a robot compared to using no signal or head
movement signal this can be seen in Figure 23 showing the score difference
between no signal and indicators for each participant. However, there was no
significant difference between how quickly the robot was understood between head
movement and no signal or indicators and move head. Specifically, these results
suggest that humans are able to understand the navigational intent of a robot using
indicators instead of no signal significantly quicker. Finally, the results also suggest
that humans aren’t able to understand navigational intent significantly quicker using
head movement instead of no signal or using indicators instead of head movement.
5.3.2 Questionnaire Section 4
The first question of this section was asking which of the behaviours the
participants would most like to see implemented as standardized behaviour for
robots displaying navigational intent. Six people answered with indicators and four
with head movement. However, all of the participants who listed their experience
with robotics as little or none also listed head movement, a few stating “as it is the
most natural”, whereas all who listed their experience with robotics as some or more
all choose indicators with statements such as “indicators as it is a well know
framework for changing and could have the most public understanding” or
0
1
2
3
4
5
NO S IGNA L S P E E D INDIC A T ORS S P E E D
PARTICPANT SCORES DIFFERENCE FOR
QUESTION 3
Alyxander David May
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“indicators as they can be seen at a glance”. The clear split in opinion dependant on
experience with robotics is an interesting point that arose, the experience with
technology however, seemed to have no over bearing factor on the response to the
question and similar to that of the work by Hall et al. who found a bias in peoples
comfort with robots will self-confessed experience (Hall, et al., 2014).
Second question asked if the participants could think of an alternative signal
of navigational intent they would prefer to those trailed in the experiment. Two of the
participants stated they would like to have a verbal signal as well as visual, one
stating “… also more accessible to the visually impaired”. One participant stated
“have a standard side, like on the UK roads (robots could) pass on the left”. Lastly
one person stated “(have a) small sideways movement before turning”. The point of
this experiment was to look into non-verbal was of expressing navigational intent,
but a verbal and non-verbal method could be used, and two of the participants feel
this would be beneficial, this is similar to the finding of Peters et al. who found some
participants would have like a verbal signal instead (Peters, et al., 2011). The
response about passing on one side is an interesting notion and not one that was
thought of for this experiment, it would however require a consensus in a large
population to be useful, also for the purpose of this experiment it wouldn’t have been
used as it would have required explaining more about the experiment to participants.
Finally, a small sideways notion again wasn’t consider in the experiment, but could
be researched, a conversation with the participant after concluded that it wouldn’t
be an ideal solution as it isn’t “obvious” enough in that people may not notice it they
were distracted.
The final question asked if any additional actions by the robot was noted in
any of the trails. Three participants provided an answer to this question: “sometimes
the robot reacted quicker to my presence than in others”, “she (Linda the robot) went
too far in one direction to avoid me” and “it felt like she followed me for some time
on the 6th test”. For the first two questions, it was explained to the participants after
the experiment that the robots behaviour was not reactive and the only time it would
become reactive was if they got extremely close to the robot, it would attempt to
circumnavigate them rather than follow its path. This happened for the final answer,
the robot went right (using no signal) and the participant did the same, once the
robot got two close it tried to plan a new goal and went around 360o on the spot the
Alyxander David May
Page | 35
same direction as the participant, giving the perception they were being followed.
After these behaviours were explained, the participants had no additional points to
bring up.
5.3.3 Robot Data
Figure 24 - Average Minimum Distances
For the mean distances, each behaviour has been looked at rather than each
test i.e. Indicators is indicate left and right etc. this way biased can be lower of a
person circumnavigating further to the left than the right for any reason. The results
show that participants kept furthest away from the robot during the indicate
behaviour with a mean distance of 1.52m ± 0.11, flowed by the head at 1.14m ±
0.09 and finally no signal at 0.98m ± 0.09. The straight behaviour was also include
in the experiment, but wasn’t include in any of the behaviours, the average distance
kept was 1.02m ± 0.13.
T tests were performed on the corresponding data, with left and right tested
against each other e.g. head left & head right and indicate left & indicate right but
will be listed as move head and indicate.
Alyxander David May
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 There was an extremely statistically significant difference in the
distances between Indicators (M = 1.52, SD = 0.50) and No Signal (M
= 0.98, SD = 0.41) with the conditions; t (19) = 5.78, p = >0.0001
 There was a statistically significant difference in the distances
between Indicators (M = 1.52, SD = 0.50) and Move Head (M = 1.14,
SD = 0.42) with the condition; t (19) = 2.38, p = 0.0277
 There was no statistically significant difference in the distances
between Move Head (M = 1.14, SD = 0.42) and No Signal (M = 0.98,
SD = 0.41) with the conditions; t (19) = 1.11, p = 0.2801
These results suggest that indicators do affect significantly how close
humans get to a robot while passing, compared to using no signal and head
movement, however using a head movement instead of no signal there is no
statistical significance. Specifically, these results suggest that humans will pass
further away from the robot when it uses indicators to signal navigation intent
compared to no signal or head movement, and that passing distance is not
statistically different comparing head movement and no signal.
Figure 25 - Behaviour Distribution
Figure 25 shows a box and whiskers diagram for the minimum distances for
each behaviour. It can be seen that the ranges of Move Head and No Signal are
Alyxander David May
Page | 37
very closely related, with a slightly high range for move head, the interquartile range
(IQR) is much the same with a slightly more contained IQR for No Signal slightly
more contained, both have a similar medium. On the other hand, the plot for
Indicators is quite different, the is similar, however, with a significantly higher
maximum and lower minimum compared to move head and no signal, the same can
be said about the IQR which is again much the same size but shifted to the right.
However Q3 and maximum are much further from the medium for indicators,
showing there is a large spread in the larger 50% of the data compared to the
smaller 25%.
5.3.4 Summary
The hypotheses previously stated were:
1) Humans feel more comfortable and are able to quickly and correctly
understand the intention of a robot when using indicators to express
navigational intention than no signal
The new empirical data gained supports this hypothesis. There is statistically
significant difference in the comfort humans feel as well as the speed and ease they
understand the navigational intention of a mobile robot using indicators compared
to no signal. It also appears to be a positive difference, in the mean a mode scores
for indicators for each question being higher than that of no signal.
2) Humans feel more comfortable and are able to quickly and correctly
understand the intention of a robot when using its head to express
navigational intention than no signal
The data gained, neither supports nor disproves this hypothesis. There is a
statistically significant difference in the comfort felt for participants between move
head and no signal, which appears to be in favour of more comfort felt during the
move head behaviour due to the higher mean and mode score. On the other hand,
there is no significant difference in the scores for the t-test between the move head
and no signal behaviours, this trend continues into the mean scores, although the
no signal mean is lower for both questions, it is a negligible amount, unlike the mode
which again remains higher with two points more.
Alyxander David May
Page | 38
3) Humans will move further away from the robot when it is indicating
navigational intent than when using no signal
The experiment data supports this hypothesis, the t-test value shows
significant differences in the distances kept from the robot to in the behaviours of
indicators and no signal. Specifically, the data shows that humans kept a greater
average distance from the robot when using indicators to show navigational intent,
the mean is significantly greater for indicators as. Furthermore, the box and whisker
plots shows that the spread is shifted to the right with a larger upper range of
minimum distances kept during the indicate behaviour.
4) Humans will move further away from the robot when it is using its head
to express navigational intent than when using no signal
For the final hypothesis, the data again neither supports nor refutes the
hypothesis. There is no significant difference in the minimum distance kept between
no signal and move head. The means again are very similar, although the mean for
no signal is lower again, the difference is not a significant one. The box and whiskers
diagram, shows how similar the data really is, with near on a carbon copy for the
ranges, medium and interquartile range.
Following on from the experiment it is suggested that the favoured behaviour
for: ease of understanding, speed of understanding and comfort felt of is indictors,
followed by move head and lastly no signal; which appears to conform with the
findings of Peters et al. although they didn’t implement the indicators a large amount
of their user group suggested them (Peters, et al., 2011). This is based on the mean
statistics for the post experiment survey. The order is also the same for the
minimum distances kept, with participants staying furthest away while the robot was
indicating followed by moving head and lastly no signal.
5.4 Limitations
One limitations during the research is the size of the experiment, with only
ten participants only seventy new sets of data have been acquired. As well as the
experiment missing depth of participants it is also missing breath, with 9/10
participants being male and again 9/10 being students, taken as a pilot experiment
interesting information has been acquired and is appears as a good starting point
Alyxander David May
Page | 39
for further research. Furthermore, half of the participants had previous contact with
the used robot that same day, this may influence how comfortable the person felt
around the robot if that had previously contact that day. However, the contact was
not related to the purpose of this experiment, although half of the participants had
previous contact with the robot, they had no additional information on the nature,
purpose or requirements of this experiment, but again with the statement of Hall et
al. this experience may be enough, more so with using the same robot to cause bias
(Hall, et al., 2014).
A major limitation is that some key aspects of comfort in HRSI as defined by
Kruse et al., including speed and acceleration are not considered and left constant.
Comfort is a very specific feeling and has been shown to be greatly be affected by
various factors, whilst trying to keep as many factors constant as possible, it may
have had a negative effect in that one behaviour may be preferred at the current
speed or acceleration used rather than in general (Kruse, et al., 2012). The same
can be said for the robot used in the experiment, using another robot the effects of
various behaviours may have had a different effect on the comfort felt by the
participants.
Another limitation stems from the hardware of the actual robot and its
positioning. The perception and understanding may be effected from the positioning
of the hardware and the way it signals. To remove as much biased as possible the
indicators were made as “standard” as possible; this was achieved by using similar
size indicators to that of a motorcycle, at a rate of 2Hz using an amber light. The
head behaviour only rotates the internal of the head; again to attempt to remove
biased, the indicators were mounted as close to the head as possible, as not to
cause people to only look for indicators.
Before the experiment, participants were instructed to change positions with
the robot in as “casual and natural manner” as possible. This statement along with
the setup of the laboratory may have led to difference in the way participants
interacted with the robot in terms of naturalness of interaction in a given direction.
However, looking at Figure 10, the average distances kept from the robot during the
left and right trails for each behaviours, aren’t significantly different. A similar
limitation was noted by Dondrup et al., however their conclusion were also that the
Alyxander David May
Page | 40
message to participants before the start of the study showed a negligible effect on
results (Dondrup, et al., 2015).
One major limitation came from the speed at which some participants
completed the first trail in their test set, due unfamiliarity with the robot. On two
occasions the participant had walked with such velocity that they had passed the
robot before it had made its navigational movement. On the other hand there were
occasions when participants walked with so little velocity that the robot had
completed its path change before the participant was near the robot. However, the
order of the trails were randomized, so this should have mo major swing on one
particular test set.
Finally, a large part of the data is based on information from the
questionnaire, of which a Likert Scale is used. Using a Likert scale it is important to
remember that the difference between each value may not be equally weighted in
the views of the user. E.g. is the difference between ‘Strongly Agree’ and ‘Agree’
the same difference between ‘Neutral’ and ‘Disagree’. However the Likert scale is a
commonly used medium that many participants would have seen at some point
before. The use of a five point scale also reduces the amount of swing that people
can feel compared to a larger scale.
5.5 Future Work
The first extension to this work would include attempting the experiment again,
with a more refined testing strategy as learnt from this iteration. The experiment
would need to contain a significant increase in participation, thus allowing for a
greater confidence in any results gained. Another requirement would be a more
diverse participant group, with specific interest regarding variance in self-defined
robotics knowledge.
Another possible extension to the project could be the implementation of addition
signal(s). This work looks at using three signals, extensions could include looking at
using different signals. One could be using a screen with images of an arrow pointing
in the direction of intended direction. A second as learnt from the user survey could
be the addition of an audio signal, this has the potential to open many paths e.g.
what is the most preferable audio signal (also suggested in study by Peters et al.)
“I’m going left now”, “please go left human” etc. or the possibility of blending with a
Alyxander David May
Page | 41
non-verbal method such as adding clicking to indicator or a simulated loud motor
noise to show turning (Peters, et al., 2011). A third option could be a sideways
movement before then intended path change, again similar to that implemented by
Peters et al.
Similar to the work of Pacchierotti et al. speed, acceleration and passing
distances could be experimented with alongside the indicators or head movement
possibly using a stationary participant (Pacchierotti, et al., 2005). Part of this stems
from the results of the minimum distances kept, when using the indicators humans
kept further away from the robot, it would be interesting if they expect the robot to
also circumnavigate at a further distance to feel most comfortable whilst using
indicators, as well as the speed and acceleration used during the experiment.
This work could be mixed with that of Lichtenthäle et al. to look at navigational
signals during a crossover scenarior rather than a pass by. (Lichtenthäler, et al.,
2013). Similarly, if the experiment took place in a real world environment the factors
would be incredibly different and more natural. The experiment by Dondrup et al.
gave a scenario of the participants and the robot waiting tables and passing each
other in corridors (Dondrup, et al., 2014). Although the experiment was implemented
in a laboratory, either the principle of setting the participants a task, or using a real
world experiment in a restaurant could be used. These could be options to gain “real
world” experience and review the implantations in a more stressful set of
circumstances to see if the results are the same. A mixture of these two project
experiments could also be used, in terms of a real world path crossing experiment.
Finally, using principles from the second experiment by Dondrup et al. varying
distances could be used for the signals (Dondrup, et al., 2015). Although indicators
was favourable in terms of comfort in this experiment, it can’t be deduced that at
varying distances this would still be the preferred behaviour. Looking at varying
distances will allow a stronger conclusion to this experiment.
Alyxander David May
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5.6 Conclusion
The objectives for this project were:
1) Continue literature review to identify three methods of expressing
navigational intent that are legible, safe and effective, as well as formation of
hypotheses for later testing
The completion of this objective can be seen in Section 2, as well as the three
navigational signals spoken about during the course of the experiment: No Signal,
Indicate and Move Head.
2) Implement chosen methods using ROS in Python and testing for
reliability using either robot or a simulation environment
The source code for the methods can be seen in on the included CD with the
report or available on at: https://github.com/LCAS/navigation_intention.
3) Create and run an experiment to test human perception for each
implementation, with appropriate post-experiment survey for data gathering
The experiment was designed and run successfully, all aspects of the
experiment can be seen in Section 5. The survey forms can be found in Appendix
item a, b and c.
4) Use appropriate quantitative and qualitative methods to review data
acquired from the experiment and test against hypotheses
The data gathered was analysed, reviewed and tested against the
hypotheses, these result scan be seen in Sections 5.3.
The aim of this work was gauge how much HRI can be enhanced by
implementing navigational signals. This work implemented three method of
displaying navigation intent, to investigate how signals of navigational intent affect
the comfort felt by people around social robots as well as the speed and ease of
understanding these signals. The data from the experiments supplies strong
evidence to support navigational signals helping benefit HRSI. The results show
comprehensive evidence to support humans comfort, speed and ease of
understanding indicators as a mode of navigational intent compare to that of no
signal as well as minor positive differences between head movement over no signal.
Thus it would be argued that navigational signals can have a significant effect on
comfort felt for humans around mobile service robot.
Alyxander David May
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The work attempted to look at one of the key challenges in social robotics as
defined by Kruse et al. in their survey on Human-Aware Robot Navigation; Comfort
(Kruse, et al., 2013). The experiment found that of three implemented behaviours,
No Signal, Indicators and Move Head; Indicators were most preferable. As a side
consequence the study also found data regarding at another challenge defined by
Kruse et al.; Naturalness. Four of the ten participants that took part in the trails listed
the behaviour they wished to be made a standard for social robotics, due to it feeling
most “natural” to them.
6 Personal Reflection
At the start of this year I knew nothing about ROS and little about Python or
Ethical and Empirical research. I’ve become a more independent, self-directed and
self-motivated learner, however I’ve also learnt to ask for support if I need it. Finally,
although being an independent project, I’ve felt part of a team with my supervisor
Marc, Christian and all those doing projects as part of the STRANDS.
I was unsure what to do as my Masters Project and after speaking to Marc I
was fairly sure I wouldn’t have the skillset required to undertake this project.
However with his and Christian’s drive, enthusiasm, wisdom and support I feel more
confident than ever. The start of the project for myself was easiest, I know how to
write a proposal, and the theory and psychology behind what I was doing and why
was clear to me. Thus for this part I was happy to continue fairly independently
checking in with Marc and Christian ever two weeks at our meetings. Once the
practical part began, I carried on independently. This could have meant the failure
of this project; while the support and guidance I got during scheduled meetings was
invaluable, instead of asking questions and checking in in-between meetings I
carried on independently; I had questions I just waited until the meetings to ask.
Thankfully I became more confident in asking question and the fact that I wouldn’t
be able to continue the way I was. A key principle this work has shown me is that it
is okay to ask for help when needed and people are a lot more responsive to it rather
than waiting for weeks before asking.
Personally, I believe the project went well and I am proud of the work I
produced. I’ve shown to myself the ability to quickly pick up a new programming
Alyxander David May
Page | 44
language, operating system and middleware, in the future I will be able to refer to
how flexible I am in project work using new systems thanks to this project. I believe
the implementation of the navigation signals are useful, novel and intuitive and they
led to an excellent overall display of the robot.
For myself the experiment was where I gained the most experience, I’ve
participated, but never run an experiment before. I got fantastic support from my
colleges on how to setup and run an ethical experiment. In my opinion the setup
was clear, concise, intuitive and interesting. I believe the participants were treated
ethically and respectfully, as well as being fully aware of their rights. Personally, I
have learnt the difficulty, stress, time and pressure it takes to setup and source
participants in an experiment. The experience I have gained from this will help me
if I chose to continue my interest in social robotics. I also feel honoured with the level
of trust shown in me, with being shown how to control the robot for the experiment,
and to record the data I needed, then being allowed to continue my experiments
unsupported.
In September, I had zero idea of what I wanted to do after this year of
University, whether I would get a graduate job, attempt to continue in academia or
go back to working with the disabled. This year has shown me a way I could do
aspects of these together. I’ve gained skills I never thought I would have and totally
enjoyed myself whilst doing it. I would like to finish by saying social robotics is fun.
Alyxander David May
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Alyxander May MAY11213081 MComp Project
Alyxander May MAY11213081 MComp Project
Alyxander May MAY11213081 MComp Project
Alyxander May MAY11213081 MComp Project
Alyxander May MAY11213081 MComp Project
Alyxander May MAY11213081 MComp Project
Alyxander May MAY11213081 MComp Project
Alyxander May MAY11213081 MComp Project
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Alyxander May MAY11213081 MComp Project

  • 1. Conveying Robot Navigation Intention to Humans Alyxander David May MAY11213081 Computer Science, MComp The University of Lincoln April 2015
  • 2. Alyxander David May Page | ii Conveying Robot Navigation Intention to Humans Alyxander David May
  • 3. Alyxander David May Page | iii Acknowledgments First and foremost I would like to thank my supervisor Dr Marc Hanheide, even before the onset of this project he showed the faith in me that I needed to push through. Without his invaluable support in robotics and more so the correct research methodologies to follow surely this project couldn’t have been achieved to this level. Without Christian Dondrup’s support and in depth knowledge of aspects ROS, Python and much more I wouldn’t have been able to meet my deadlines. Always approachable and able to offer help when needed his expertise were of vital importance to me. Matthew Ashton for providing me with all I needed to create a functional workspace to continue on my work through to the small hours of the morning. A thanks to all the members of the STRANDS team for all the hard work they have done in developing the Scitos G5, to get it to the current state and allow this project to be run using one of their robots. Lincoln Centre for Autonomous Systems and Professor Tom Duckett for allowing me to use their office and workspaces for the lifecycle of this project. Finally, a thanks to all my fellow peers for pushing me through this project; special thanks to all those who participated in the experiment.
  • 4. Alyxander David May Page | iv Abstract The general aim is to gauge and measure in terms of comfort felt and ease of understanding, how human-robot interaction can be enhanced by implementing and empirically evaluating methods of expressing navigational intent on a mobile robot. Three behaviours were developed for displaying navigational intent: No Signal, Indicators and Move Head. These behaviours were then evaluated by designing and running an experiment to empirically evaluate them. The robot used was a Scitos G5 with a Human Machine Interface and Indicators attached. The experiment ran over the course of three days with ten participants, who each filled out a survey attempting to ascertain: how easy it was to understand the intent of the robot, how quickly they could understand the robot and how comfortable they felt around the robot. Of the behaviours Indicators was the most preferred in each aspect, with a statistically significant different mean difference compared to No Signal, and a higher average score on each aspect than Move Head. The minimum distance kept from the robot was also analysed, with participants keeping a statically significant further distance when the robot was using Indicators compared to No Signal, and an average further distance compared to Move Head. Participants were also asked to choose which of the behaviours they would most like to see implemented as a standard for social robotics, four selected Move Head a believed it felt most natural all with self-confess little or no knowledge of robotics. While six selected Indicators, as they believed it is the most obvious, most with some or more robotics knowledge.
  • 5. Alyxander David May Page | v Table of Contents Acknowledgments ....................................................................................... iii Abstract .......................................................................................................iv List of Tables & Figures.............................................................................. vii 1 Introduction ............................................................................................ 1 1.1 Background ..................................................................................... 1 1.2 Aims & Objectives ........................................................................... 2 1.2.1 Aim............................................................................................. 2 1.2.2 Objectives .................................................................................. 3 1.3 Rationale ......................................................................................... 3 2 Literature Review ................................................................................... 4 2.1 Human-Robot Interaction ................................................................ 4 2.2 Human-Robot Spatial Interaction .................................................... 6 2.3 Robot Navigational Signals ............................................................. 8 3 Design.................................................................................................... 9 3.1 Project Management ....................................................................... 9 3.1.1 Traditional.................................................................................. 9 3.1.2 Supervision & Deliverables ...................................................... 11 3.1.3 GitHub...................................................................................... 12 3.2 Research Methodology.................................................................. 12 4 Software Development......................................................................... 16 4.1 Tools & Machine Environments..................................................... 16 4.1.1 Robot Operating System.......................................................... 16 4.1.2 Ubuntu ..................................................................................... 16 4.1.3 Python...................................................................................... 16 4.1.4 Robots ..................................................................................... 17 4.1.5 SCITOS G5.............................................................................. 19
  • 6. Alyxander David May Page | vi 4.2 Development Methodology............................................................ 20 4.2.1 Incremental Development........................................................ 20 4.2.2 Action Lib................................................................................. 21 4.2.3 Testing..................................................................................... 22 5 Experiment........................................................................................... 23 5.1 Design ........................................................................................... 23 5.1.1 Physical ................................................................................... 23 5.1.2 Robot Setup............................................................................. 24 5.1.3 Participants .............................................................................. 25 5.2 Results .......................................................................................... 26 5.3 Evaluation...................................................................................... 28 5.3.1 Questionnaire Sections 1, 2 & 3............................................... 28 5.3.2 Questionnaire Section 4........................................................... 33 5.3.3 Robot Data............................................................................... 35 5.3.4 Summary ................................................................................. 37 5.4 Limitations ..................................................................................... 38 5.5 Future Work................................................................................... 40 5.6 Conclusion..................................................................................... 42 6 Personal Reflection.............................................................................. 43 7 Bibliography ......................................................................................... 45 8 Appendices .......................................................................................... 51
  • 7. Alyxander David May Page | vii List of Tables & Figures Figure 1 - Traditional Project Management (Project Lifecycle Services Ltd, 2014)...................................................................................................................... 9 Figure 2 - Empirical Research Cycle (Explorable, 2015) ........................... 13 Figure 3 – Rovio (Robotdom, 2015)........................................................... 17 Figure 4 – Turtlebot (Turtlebot, 2015)........................................................ 18 Figure 5 – inMoov (3diot, 2014)................................................................. 18 Figure 6 - Pioneer 3 – AT (Unmanned Vechicle Centre, 2015) ................. 18 Figure 7 – Scitos G5 (Lincoln Centre for Autonomous Systems, 2015)..... 19 Figure 8 - Robot Comparison Matrix.......................................................... 19 Figure 9 - Waterfall vs Incremental (Bittner, 2006) .................................... 20 Figure 10 - Client-Server Interaction (ROS, 2015)..................................... 21 Figure 11 - ROS Topic Graph.................................................................... 22 Figure 12 – Move Head Behaviour: left, Straight, Right............................. 24 Figure 13 – Indicate Behaviour: Left, Straight, Right ................................. 24 Figure 14 - Available Robot Paths ............................................................. 25 Figure 15 - Survey Results ........................................................................ 27 Figure 16 - Robot Area of Interest for Minimum Distances........................ 27 Figure 17 - Minimum Distance from Robot (m).......................................... 28 Figure 18 - Results Question 1 .................................................................. 28 Figure 19 - Score Differences for Question 1 ............................................ 29 Figure 20 - Results Question 2 .................................................................. 30 Figure 21 - Score Differences for Question 2 ............................................ 31 Figure 22 - Results Question 3 .................................................................. 32 Figure 23 - Score Differences for Question 3 ............................................ 33 Figure 24 - Average Minimum Distances................................................... 35 Figure 25 - Behaviour Distribution ............................................................. 36
  • 8. Alyxander David May Page | 1 1 Introduction 1.1 Background Robots are becoming increasingly more common in industrial environments “…up to 2008 about 63,500 service robots for professional use were sold during a period of more than 12 years. However, during the past five years some 100,000, service robots for professional use were sold according to the results of these statistics.” (International Federation of Robotics, 2014). Alongside this it is also believed that robotics for the elder and disabled will increase with a forecast of 12,400 units to be sold in 2014-2017, “This market is expected to increase substantially within the next 20 years.” (VDMA, 2013), with this increase in robots it becomes more important to look at Human-Robot Interaction (HRI). A key principle of mobile robotics is to achieving safe operation in the presence of humans (Steinfeld, et al., 2006). Robots currently are successfully able to navigate through environments including circumnavigating obstacles. However, humans must be treated differently to ensure they feel safe and comfortable around robots. “To improve the robot behaviour, we conducted a human-human experiment to find a socially plausible strategy to behave in such situations.” (Kruse, et al., 2012). A study conducted by Kruse, looked at how people perceived each other’s movements in a path crossing scenario and what they preferred. This is where the need for Human-Robot Spatial Interaction (HRSI) has come in, not only do robots need to be aware of humans, but they need to be traversed in a way they are comfortable with. By implementing a direct navigational intention signal from a robot, humans will have a better understanding of what the robot intents; which leads to better perception and comfort with robots. A study carried out by Breazeal et al, identified that humans overall had a more positive interaction with a mobile robot which game them non-verbal signals, i.e. a head nod or an eye blink, compared to that of no signal (Breazeal, et al., 2005). With the advance in robotics, we increasingly use robots to fulfil tasks we as humans consider too dangerous, fatuous or inherently tedious. One area this can be seen is in the care industry, where HRSI is paramount for safety of people either ill, disabled or elderly. The STRANDS project, an EU commissioned research
  • 9. Alyxander David May Page | 2 project is currently delving into this environment “STRANDS will produce intelligent mobile robots that are able to run for months in dynamic human environments.” ( Automation and Control Institute, 2015). One of the partners for this project is Haus der Barmherzigkeit (translates to ‘House of Mercy’) an Austrian elderly care home in Vienna. Clients of Haus der Barmherzigkeit may suffer from various cognitive and/or motoric disabilities, this may mean they have to impaired movement and/or judgment (Haus der Barmherzigkeit, 2014). Therefore, a smooth easy to understand action from a mobile robot in their environment is imperative to their comfort; more so as it will be their own home. Another part of the STRANDS project is looking into mobile security working alongside security guards “This is the first time that an autonomous robot has been deployed in a working office environment to do a real job.” (G4S, 2014). Again, in this environment it is imperative the robot is aware of humans and how to interact and navigate around them, with other humans also competing tasks alongside them. 1.2 Aims & Objectives 1.2.1 Aim The general aim is to gauge and measure in terms of comfort felt and ease of understanding, how human-robot interaction can be enhanced by implementing and empirically evaluating methods of expressing navigational intent on a mobile robot. Three different methods of expressing navigation intention will be implemented and empirically reviewed. With these implementations it is hoped improvements can be made in how humans perceive the navigational intent of mobile robots through more legible signalling, as well as giving humans more confidence working around or with mobile robots as they become part of everyday life. By implementing various techniques of non-verbal communication, hypotheses surrounding each of these implementations will be formed. An experiment will be used to ascertain the research data. By applying quantitative and qualitative research methods hypotheses will be tested and evaluated, to see if there appears to be any significant differences between the implementations. Some of the key aspects to look at are: ‘Ease of understanding the behaviour’, ‘Speed of understanding the behaviour’, ‘Was the subject comfortable
  • 10. Alyxander David May Page | 3 around the behaviour’, etc. As can be seen from the questions, the main aspect is to ascertain participant’s views on the various behaviours and is a very person centred approach, with the participant being the key factor and not how the robot responds. 1.2.2 Objectives 1) Continue literature review to identify three methods of expressing navigational intent that are legible, safe and effective, as well as formation of hypotheses for later testing 2) Implement chosen methods using ROS in Python and testing for reliability using either a robot or a simulation environment 3) Create and run an experiment to test human perception for each implementation, with appropriate post-study survey for data gathering 4) Use appropriate quantitative and qualitative methods to review data acquired from the experiment and test against hypotheses 1.3 Rationale The study is inherently a research project in that the key principle includes divulging into an area which is currently strongly researched; HRI and more specifically HRSI. With being a research project it is important to understand that the key components are the results attained during the experiment and the reflections they have on the implemented system, rather than the system as a whole. The hope of the project is to answer questions about how signals can improve the comfort felt by humans around mobile robots. The work will look to answer questions that have been purposed by Dondrup et al. by looking at the psychology behind using different signals to achieve more comfortable navigational intent (Dondrup, et al., 2015). Principles will be used including signals previously implemented and discussed by Peters et al., who have left scope for their research to be continued alongside Dondrup et al.’s with signals being implemented from both (Peters, et al., 2011). This gap in research can be seen in a recent survey of Human aware navigation by cruse et al, the finds much in the way of robots navigating to and around humans and the comfort attached with this, navigational intent signals is not addressed (Kruse, et al., 2013).
  • 11. Alyxander David May Page | 4 2 Literature Review One of the biggest issues to remember whilst conducting any research with robots is that they have limitations and you have to work within these limitations. As described by Dudek and Jenkin “It is interesting to note that fictional robots usually do not suffer from the computational, sensing, power, or locomotive problems that plague real robots.” (Dudek & Jenkin, 2010). Although, this work shouldn’t be resource hungry, it is still important aspect to remember in any robot related research, it might not be perfect due to various limitations. On the other hand it is also important to understand the scope of current robotics. They range from replacing the aging workforce in the automobile manufacturing industry (Byrant, 2014) to helping interpretation and the transport of food to people in West Africa aiding the Ebola outbreak (Gaudin, 2014) and helping in with bomb disarmament and disposal in war zones (Defence, 2014) and many more in between. For the purpose of this project a mobile personal robot will be used. 2.1 Human-Robot Interaction Within social robotics, some people are more experienced in interacting with robots than others, this can lead to possible positive and negative connotations for research, but also biased. A study conducted by Hall et al., concluded that “Participants who self-reported greater robotics knowledge reported higher overall engagement and greater success at developing a relationship with the robot.” (Hall, et al., 2014). This study shows the importance of ascertaining people’s robotics experience in social robotics research. This work wishes to look at the comfort felt by participants (amongst other aspects), thus it is important during the experiment to ascertain the robotics experience of participants. Human perception is constantly changing for robots, this can lead to a more natural and comfortable interaction for humans. Hansen et al., show this change in perception by researching into gesture recognition with mobile robots, specifically they attempt create a robot system that analyses a participants posture and interact with them accordingly (Tranberg Hansen, et al., 2009). The system shows clear machine learning with the ability to adapt to different human behaviours over time. A similar study by Svenstrup et al. uses a similar approach to gauge human interest
  • 12. Alyxander David May Page | 5 in an interaction; using a three variable Case Based Reasoning system similar to that used Tranberg Hansen et al. (Svenstrup, et al., 2009). Both of these studies show examples of a robot learning to perceive humans in different scenarios to understand their needs. This work intends to look at the inverse; looking at how humans perceive the robot and understating the needs and intention of the robot in the form of navigational intent. Robot signalling is an area increasingly research, this can be seen in the work of Mutlu et al., who investigate the psychological effects of a robot using gaze cues for a turn taking conversation in a group (Mutlu, et al., 2009). The study concludes those in the conversational groups “…also felt more acknowledged, welcomed, and valued by their group, and that they belonged more to the group…” Similar work has been done by Bennewitz et al.; who implemented an informative humanoid robot to give information to patrons at a museum (Bennewitz, et al., 2005). The experiments found that “Almost all people found the eye-gazes, gestures, and the facial expression human-like and felt that Alpha was aware of them.” (Alpha was the humanoid robot used in the study). Both of these show humans feel more involved in a conversation situation when receiving signals or gazes from a robot. It’s the hope of this work to see if these concepts can be transposed to navigational signals; to increase the comfort felt for humans by receiving navigational signals. Another point raised by these studies is eye gaze feeling natural, as such; eye gaze will be one of the signals used in this study to display navigational intent. A study at Massachusetts Institute of Technology by Breazeal et al., conducted an experiment in which participants were tasked to teach a robot the location of buttons and to press the corresponding button (Breazeal, et al., 2005). The control group received no feedback from the robot, whereas the other test group received a visual facial expression, a nod for understanding and a confused face for non-understanding. “H1: Subjects are better able to understand the robot’s current state and abilities in the IMP+EXP case”, this hypothesis states the authors believe participants will be able to better understand the robot when it expresses understanding using facial expression. From the results it was concluded “There was a significant difference between the two manipulators on answers to questions about subject’s ability to understand the robot’s current state and abilities. Thus
  • 13. Alyxander David May Page | 6 Hypothesis 1 is confirmed…” The study shows that humans are better suited to understand the intention of a robot when it is display signals to them. In this work, it is intended to follow on using these principles to see if they apply to other aspects of HRI specifically Spatial Interaction and whether signals improve the understanding navigational intent. 2.2 Human-Robot Spatial Interaction Torta et al., conducted an experiment in which a human sat with a humanoid robot approaching from various angles and distances to initiate communication (Torta, et al., 2011). The study showed that the optimal approach angle for participant comfort was directly in front rather than from the side. A similar study, by Walters et al., use standing participants and a mobile robot (Walters, et al., 2011). The effects looked at here are long term comfort rather than short term, results showed that participants felt more comfortable approaching the robot in a confined space compared to being approach by the robot in the same confined space. This work will continue on the themes shown in these studies, the principle will be taken from Torta et al., to use a head on encounter, similar to a corridor passing behaviour, whilst also allowing the participants to move in and around the robot in whatever way feels comfortable to them; the work attempts to build on what has been previously shown by trying to investigate in which scenarios humans feel more comfortable around mobile robots. The distance, speed and direction a robot moves in relation to a human is important in achieving the most comfortable HRSI. Work by Pacchierotti et al., looks into the feelings of humans at varying distances and speeds in a hallway setting (Pacchierotti, et al., 2005). The results show that “The best behaviour was, according to all the subjects, behaviour 8 (see Table III), i.e. the one with highest speed and largest signaling and lateral distances”. Although the scope of this work doesn’t include looking into varying speeds and distances, it is an important point to note in that participants’ feelings may be greatly affected by the speed and or distance the robot takes in in avoidance path. For this reason, it will be established that the same speed, angle and path will be taken by each different behaviour, to ensure consistency.
  • 14. Alyxander David May Page | 7 What kind of behaviour do humans expect from moving robots? One aspect of this question, a path crossing scenario was investigate by a Lichtenthaler et al. they attempted to look at how humans wanted a robot to react in a direct path crossing situation and to test various implementations they created an experiment (Lichtenthäler, et al., 2013). The authors concluded that the ideal situation is where the robot the heads directly towards the goal and only changes its path by halting if it were to invade the personal space of a crossing human. This was also the scenario in which the participants felt most comfortable around. A similar study by Dondrup et al., looked at whether hesitation signals could be seen in as path crossing scenario (Dondrup, et al., 2014). The results show “that hesitation signals can be found in head-on encounters during pass-by scenarios”, these could lead to increased stress and less comfortable feelings around robots. In both of these experiments, the concept of navigation intent signals could have made a difference to how comfortable and confident participants felt around the robots. This work will attempt to look at the comfort felt during the pass by scenario and evaluate any differences between various forms of navigational intent. One way being investigated of improving the comfort felt by humans around social robots it to look at robots exhibiting more “natural” behaviour. Saulnier et al., look at using robot body language to catch people’s attention. In their study a robot approach a group of two people and exhibited one of three behaviours: ignore and pass by, approach with quick and erratic behaviour or approach slowly. The participants confirmed they felt navigational behaviour conveyed messages from the robot (Saulnier, et al., 2011). Althuas et al. conducted a study in which a robot approached a group of people, by approaching the middle of the group body facing toward the group. The subjects felt the behaviour exhibited by the robot felt natural (Althaus, et al., 2004). Finally a study by Satake and Hayashi conducted experiments with of robots approaching humans in a shopping centre (Satake, et al., 2009) & (Hayashi, et al., 2011). They found people felt it was most comfortable for the robot to approach from the front. It is important to remember the principle of “The Uncanny Valley” in which Mori describes the phenomenon of robots becoming too human-like can cause discomfort (Mori, et al., 2012). These studies show that humans do feel more conferrable around robots conforming to natural human behaviour; to an extent. One of these natural behaviour is to look where we are
  • 15. Alyxander David May Page | 8 going, this can be implemented on a mobile robot to “look” in the direction it intends to move as a form of navigational intent. 2.3 Robot Navigational Signals Peters et al., performed a study based around a pass by scenario in a hallway, participants were asked how they felt about the manner in which a mobile robot should express navigational intent (Peters, et al., 2011). The navigational signals used in the experiment were: sideways motion, forward or backwards motion, stopping, screen signals or camera motion. The results showed that forwards, backwards, and sideways motion was were favourable with a combine 49% of people preferring them, compared to 22% for screen signals and less than 10% each for the rest. A question in the survey asked users if there was another way they would like to see navigational intent expressed, 60% of people responded with indicators. Indicators are a simple but effective and well known medium for expressing navigational intent that most people will be aware of from transport, it would be implemented onto a robot for the study. The second experiment, in a study conducted by Dondrup et al., attempts to look at the way in which humans interact with a robot in a corridor passing scenario (Dondrup, et al., 2015). Two participants were involved, both human however, one was dressed in a robot costume with their eyes and face hidden. The “robot” received instructions on movement and collision avoidance via a set of headphones. The other participant was naïve to the end goal of the experiments, they were just instructed to cross the corridor without colliding with the “robot” and with as little movement as possible. The robot had a tablet positioned on its chest after the start of each trial could either display eyes, looking right or left, indicators again right or left or no signal. At no point during the study did they attempt to deceive the participant i.e. indicate left and then move right. The robot would also change when it would signal from either 1s, 1.5s or 2s into the test. The purpose of the study was to investigate different aspects of HRSI using Qualitative Trajectory Calculus state chains, however the psychology aspects of the difference in human interaction during the second experiment weren’t looked at. The authors stated “Some of the more interesting phenomena in the experiments, especially the “Bristol Experiment”, like if the indicators had an effect on the interaction between the two agents or if the timing was important for the use of the indicators, will be investigated in more
  • 16. Alyxander David May Page | 9 psychology focused work.” (Bristol Experiment refers to the experiment spoken about in this paragraph, the second experiment of the work) The experiment for this work will attempt to follow the same strategy of the second experiment, keeping as many factors the same as possible, however for the purpose of this work; only the various signals will be looked at not the timings as well thus just having one variable. Two of the behaviours present in this study will be used, the indicators and the eyes. However, the will be physically implemented rather than using a screen. 3 Design 3.1 Project Management 3.1.1 Traditional This project will use the traditional project management approach. As the project is a research project, with a coding, experiment and evaluation it seems appropriate to use a rigid methodology. As the project is neither client nor user driven, an agile methodology isn’t needed, as the requirements are unlikely to change in any significant manner until the entire process is completed. The stages can be broken down into: Aim & Objectives (Business Requirements), System Requirements, Design, Development (Build), Experimental Analysis (Test) and Evaluation (Deploy). The cycles as seen below in Figure 1 is a waterfall style, with one aspect being completed before the next is started. Figure 1 - Traditional Project Management (Project Lifecycle Services Ltd, 2014)
  • 17. Alyxander David May Page | 10 Aims and Objectives has already been completed in the project proposal and are listed previously in the report. System Requirements follows, in which the literature review is a major aspect. The review shows what research has been conducted and the gaps in it, thus hinting at research that needs to be continued. The system requirements for this project will be bases around an extension on the aims and objectives and gaps in the literature review. The system requirements are as follows: 1) Implement a way of using indicators to signal navigational intent 2) Implement a way of using the head to signal navigational intent 3) Create a randomized testing strategy capable of handling a non- specified amount of subject Design is the next aspect, this will not just include software design; but also the design of the experiment and the methodologies used in the later evaluation of the project. The software will be designed around the system requirements, using established frameworks. The software for this project however is minimal, there is greater interest in the experiment than the actual software. Thus the design for the experiment is pivotal. The experiment design will attempt to remove as much bias as possible as well as running in an ethical, professional and appropriate manner; by doing this it should allow greater reliability of results attained. Finally, the appropriate paperwork for the subjects in the experiments must be design, this will include: consent form, demographic form and post experiment survey. The forms must include all relevant information as well as being informative and ethically correct by informing participants of their rights. Building again is not entirely software based, as the experiment is such a vital part. The first aspect of the build will be the software development, this allows a working prototype in either a simulation or a real world situation; giving the basis for the experiment setup. In addition to the software, additional hardware will need to be added and mounted to the robot; 5V indicators will be added to allow the robot to indicate, as well as a rotating head. Additional changes to the software will be made after the addition of the indicators and head. Following on from the software and hardware the experiment will be designed, as well as minor changes to the software to incorporate the location of the study. The experiment will be in a single
  • 18. Alyxander David May Page | 11 self-contained and controlled environment, using the same robot to allow for the greater accuracy of results. The final stage will be to record data from the robot of the position of the subject in the experiment. Experimental Analysis is one of the most important aspect of the research. All attempts will be made to remove bias from the experiment to ensure a high quality as possible data set. The experiment will occur in Lincoln Centre for Autonomous Systems research laboratory over the course of a few days. Participants will be handled on an individual basis to stop participants who have experience the experiment sharing their opinions with those that haven’t. This section also includes coagulating the data formulated in to the experiments to a format that can later be reviewed; this includes the survey questions and the positions of the participants from the robot. All the data will be tabulated and appropriate graphs and paired t-tests etc. will be used to create the final results. Evaluation is the last element, and starts by discussing and concluding on the results attained. A key part of the evaluation is to look at the limitations of the project and ways it could be improved if undertaken again. Most importantly, the evaluation should preclude to possible extensions that could be looked at with the knowledge attained from this iteration. Finally a critical reflection in which the aspects of the project management methodology will be critiqued and reflected upon as to whether they were appropriate in the context of this project, as well as what could have been done differently from a project management perspective. 3.1.2 Supervision & Deliverables To ensure that the project continues as expected and time frames are being met, regular supervision with the project supervisor will take place. Meetings will take place on at least a fortnightly basis, thus giving time for decent strides to be taken, but at the same time not long enough to fall majorly behind the goals. As well as regular meetings, non-formal contact will be used if any problems arise within the project that doesn’t require face-to-face time; this will be done using email predominately and could be for any small matters. However, should a matter arise that does require extra contact time, additional meetings can be planned on pro re nata basis.
  • 19. Alyxander David May Page | 12 At various times during the project deliverables will be needed rather than just communication, a summary of Goals and Time Frames can be found along with a Gantt chart in Appendix items D and E respectively. The main deliverables will be: software, evidence of functional software, experiment plan and results. The software will be hosted on a code revision site that the supervisor will have public access too, thus can be reviewed at any time. The software will be run in a simulator environment that can be either viewed at a meeting or a video can be sent to the supervisor; evidence of functionality will be needed before testing on a real robot. The experiment plan will be delivered with a physical showcase, including an explanation of why it has been prepared in the manner shown, and a copy of all the relevant paperwork that will be used in the course of the experiment. Results will be shown after the experiment and in the form of graphs and charts thus easier to understand and digest than raw data. Finally, all documents will be available for the supervisor to view on a file hosting service, meaning easy feedback can be given in-between meetings. As well as this a risk assessment and contingency plan list has been created and can be seen in Appendix item F. 3.1.3 GitHub GitHub is a Git repository host service. Git hub allows easy source code management (SCM), code revision, collaboration and code access worldwide (Git Hub, 2015). From a project management perspective, a code revision system is invaluable, it will ensure that the code is always kept on the cloud and accessible anywhere. As well as this, it allows easy access for code to be tested on different machines and most importantly the robot itself. Finally, it allows easy support for the project supervisor, they are able to quickly look at the code and what work has been done in-between meetings, comments can be left and they can trial the code before using it on a real robot. 3.2 Research Methodology During the course of this project, the empirical research approach will be used, empirical research with its distinctive sections ties in well with tradition project management. “Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief.” (Cahoy, 2015), empirical research stems from the philosophical medium Empiricism which is described as gaining knowledge through experience (Duignan, 2015).
  • 20. Alyxander David May Page | 13 Empirical research has been shown to work very well with social robotics and with HRSI, examples of this can be seen in the work of: Hansen et al., Peters et al., Saulnier et a., and more mentioned in the literature review (Tranberg Hansen, et al., 2009) & (Peters, et al., 2011) & (Saulnier, et al., 2011). One of the main factors of the empirical research methodology specifically in HRSI is a direct interaction experiment, for the purpose of this work it will be in a laboratory environment. This methodology has been proven in many situations such as work by Young et al., in which they evaluated a dog lead system for robots, with the robot either walking in front or following the participant (Young, et al., 2011). Other examples already spoken about in the literature review include: Dondrup et al. and the robot signalling experiment (Dondrup, et al., 2015), Pacchierotti et al. and the robot approach experiment (Pacchierotti, et al., 2005). Empirical research contains the following steps: 1) Observation Observe by collecting and analysing empirical facts 2) Induction Formulating hypotheses via Induction 3) Deduction Deduct consequences with attained empirical data 4) Testing Test hypotheses with new empirical data 5) Evaluation Evaluate the outcome of testing Figure 2 - Empirical Research Cycle (Explorable, 2015)
  • 21. Alyxander David May Page | 14 Observation for this research has been covered by doing an in depth literature review of projects surrounding the area of navigational intent and the use of signals from mobile robots. Various projects have been studied and analysed, each with a slightly different view point on the research goal, thus giving a deep yet broad underpinning to the aims of this project. The literature review has shown that, humans do much prefer a signal of navigational intent from a robot in various scenarios, as well there is a maximum and minimum “natural” distance and speed a robot should use when moving around humans. What hasn’t been addressed in much detail however, is the specifics of how different signals alter the perception, understanding and comfort for humans regarding robot navigation intent, this is the main area of concern for the project, to attempt to distinguish between various signals. Induction is using what has been seen in the literature review and forming specific hypotheses from the general view. To form the hypotheses, general principles seen in the review must be taken. The generalized principle from the review is that humans prefer a signal of navigational intent. Thus, hypotheses can be drawn from these principles. The hypotheses drawn from the literature review are: 1) Humans feel more comfortable and are able to quickly and correctly understand the intention of a robot when using indicators to express navigational intention compared to that of no signal 2) Humans feel more comfortable and are able to quickly and correctly understand the intention of a robot when using its head to express navigational intention compared to that of signal 3) Humans will move further away from the robot when it is indicating navigational intent than when using no signal 4) Humans will move further away from the robot when it is using its head to express navigational intent than when using no signal Using the general principle and induction, it has previously been shown that humans prefer navigational signals from a mobile robot. Therefore, humans would probably prefer the robot to use indicators and a head movement to no signal, as both of these are signals of navigational intent.
  • 22. Alyxander David May Page | 15 Deduction is where aspects relevant to the study are conceptualize and the formation of the experiment begins. Humans are exposed to indicators on a regular basis when either on the road in a vehicle or walking. Humans understand that when a vehicle indicates left, it intends soon to turn left. From this we can deduce the same principle and apply it to the robot, by applying indicators we can assume that when a robot indicates left it intends to turn to the left and vice versa. The same can be deduced regarding human navigation, humans generally look in the direction they are walking and intent to move, this can also be applied to robotics. We can apply a head movement to the robot moving its head and eyes to “look” at the direction it intends to travel, this should be deducted by the human as the robot looking in the direction it intends to travel. Testing will be completed by an experiment. Participants will complete a set of trails, interacting with the robot in a controlled environment. All efforts will be made to reduce the bias in the experiment to as low as possible. As well as this it will be attempted to get a diverse test data set, specific aspects that will try to be attained are a spread of ages, gender and knowledge of robotics. Furthermore, to attempt to lower any bias the order of the test will be randomized for each participant with only the controller know what will happen during the experiments. The data obtained in the experiments will be quantitative and qualitatively examined, then tested against the hypotheses formulated in the induction. Data will be attained as a mixture of software capture from the robot and opinions of the participants in the form of a survey. Evaluation starts by looking at the graphs and charts formulated by the data collected in the experiment, these will created as using means standard error of means, and mode; including other statistically accepted formulas. As participants will complete all of the seven tests, a paired t-test will be used to see if there is a statistically significant difference between the three behaviours. The hypotheses will be critiqued as well as the testing strategy. The evaluation should reference back to the literature review and discuss if findings collaborate with the research of the previous authors, as well as highlight the differences between the works. There will be a discussion into what the possible connotations of the findings (if any) are as well as looking at possible improvements if a second iteration to take place; finally
  • 23. Alyxander David May Page | 16 looking into the possible extensions of the project and further research that could be undertaken as a direct extension to the project. 4 Software Development 4.1 Tools & Machine Environments 4.1.1 Robot Operating System ROS is used for a variety of tasks: HERE maps (Delaney, 2014), Aerospace Research (Foote, 2013), Industrial Manipulators (Intermodalics, 2015), amongst others. Carrying out a research project, with real world connotations, it is imperative to attempt to use established mediums to ensure reliability, it is clear to see that ROS is not only a proven system but an adaptable one with more than enough functionality for this project. “ROS (Robot Operating System) provides libraries and tools to help software developers create robot applications. It provides hardware abstraction, device drivers, libraries, visualizers, message-passing, package management, and more.” (Willow Garage, 2015). ROS is a peer-to-peer robot middleware package, it allows easier hardware extraction and code reuse. All major functionality is extracted into separate nodes, which typically run in a separate process. Communication 4.1.2 Ubuntu The main ROS client libraries are created in accordance with Unix-like systems, predominately due to the open source nature of the software dependencies (ROS, 2015). In light of this the only supported operating system is v various Ubuntu versions dependant on the version of ROS. It is important to note that while ROS is available for Windows and Mac, they not supported and thus are maintained by the community, this is one of the key reasons that Ubuntu was the chosen Operating System for the development of the software. Ubuntu is also the preferred and supported OS for the industrial version of ROS; ROS-Industrial (ROS- I), “Like ROS, ROS-I nominally runs on Ubuntu Linux…” (ROS Industrial, 2015). 4.1.3 Python The coding language used for the purpose of the experiment was python. With ROS only two languages are available, C++ and Python. Due to the nature of
  • 24. Alyxander David May Page | 17 this experiment, the coding aspect is of minimal importance and the advanced functionality of C++ isn’t required. Although Python will take time to learn; is an interpreted language and thus doesn’t require to be complied each and every time it is run and tested, this will time will make up for the added time to learn. Alongside this, it terms of ease of understanding Python is far simpler as it more similar to simple English. All resources need for the project are available in Python, as well as a large set of tutorials and how to help on wiki ROS and other such similar websites. 4.1.4 Robots The university have various different appropriate robot systems available that could be used for this project, those considered for the project are: Rovio, Turtlebot, Pioneer 3-AT, MARC (based on inMoov robot) and Scitos G5. The robot required, will need to be mobile, capable of using indicators and a head movement to display navigational intent. The robot should also pose enough of a physical obstacle so that participants are required to circumnavigate the robot and not just walk over it. Only robots with a camera have been looked at, this is due to needing a way for the robot to track the position of the subject during the tests. Rovio is a small mobile webcam, with a speaker, microphone and articulating head capable of three different positions, and is roughly shin height. While it is mobile, there is no easy way to attach the desired indicators to the robot. Another added issue although the head can move; it only moves in the vertical axis, which wouldn’t be an intuitive way to express navigational intent. Thus this would have to be explained to participants before the experiment and giving away vital information about the nature of the experiment. Finally, the robot is far too small in stature, it could easily be walked over, for all these reasons it won’t be used (WowWee, 2015). Figure 3 – Rovio (Robotdom, 2015)
  • 25. Alyxander David May Page | 18 Turtlebot is an open source and hardware robot. Due to the nature of turtlebot, it is highly customizable. The University has turtlebots powered by an i5 Intel Nuc, the Nuc is sufficient to add the capabilities of indicators to the robot. Additional hardware in the form of an articulating head would however need to be added to the robot to make it suitable for the tasks. With turtlebot only being approximately knee, it is another robot that could be walked over instead of circumnavigated. The robot has a Kinect on board that would be able to track people in 3D space. Due to the additional hardware and size this robot wouldn’t be appropriate to use for the experiment. MARC (Multi-Actuated Robotic Companion) is a 3D printed robot based on the open source inMoov project by Gael Langevin (Langevin, 2015). MARC has a fully articulated head, fingers and arms, with speaker, a microphone, chest mounted Kinect camera, two cameras in the eyes and is capable of people tracking in 3D space. Being the size of a small person, MARC is not something that can be walked over. MARC is capable of moving the head with 120o of freedom and being open hardware based on a Pololu electronics, indicators could easily be added. The limiting factor is currently MARC is not mobile as the legs are still being designed by Gael. A possible solution to this could be to place MARC on wheels, however this isn’t a very elegant design and could subtract from the end perspective of the participants, being very unnatural looking. Pioneer 3-AT is a highly versatile four wheeled robotic platform. Similar to the turtlebot, it is highly customizable and able to add additional hardware. Out of the box the platform doesn’t contain the required hardware, a 3D camera similar to the Kinect, indicators and a articulated head would have to be added, although this is possible, it is rather a lot of hardware work. Included with this, the robot base is only shin height meaning to add a head to it, substantial fabrication would be needed to Figure 5 – inMoov (3diot, 2014) Figure 4 – Turtlebot (Turtlebot, 2015) Figure 6 - Pioneer 3 – AT (Unmanned Vechicle Centre, 2015)
  • 26. Alyxander David May Page | 19 make adding an articulated head to the system. So although the system is rugged and highly customizable, it is too much additional work for the purpose of a project like this. Scitos G5 is a robot with a full working computer and touch screen. Including additional hardware G5 stands at the height of a small person; meaning participants would have to circumnavigate. Included in the additional hardware is a head unit in which the eyes are capable of full 360o rotation. Various voltage ports are also available to power indicators. The robot also has two mounted 3D cameras capable of tracking participants in 3D space. It’s because of these reasons, along with needing little additional hardware work that the G5 has been chosen. The key aspects for each robot can be seen in Figure 8. Rovio Turtlebot MARC Pioneer 3-AT Scitos G5 Mobile ✔ ✔ X ✔ ✔ 3D Camera X ✔ ✔ X ✔ Rotating Head Up and Down X ✔ X ✔ Ports for Indicators X ✔ ✔ ✔ ✔ Large Presence X X ✔ X ✔ Figure 8 - Robot Comparison Matrix 4.1.5 SCITOS G5 Scitos G5 (aka Linda) robot with a Human Machine Interface enclosure manufactured by Metralabs Germany: Scitos G5 is about 1.5m (ca. 5ft) tall and weighs about 75kg (ca. 165Lbs.). The Scitos G5 conforms to the European CE- guidelines for the public indoor sector and was certified by the German Technical Inspection Agency (TÜV). The Scitos G5 does not have any end-effectors and is therefore only able to interact with humans via spatial movement, eye gaze, speech and a touch screen (MetraLabs, 2015). Due to other software running on the robot created by the STRANDS team, it isn’t possible for the robot to actively collide with the human, it will stop, turn around and attempt to find a new path if the human gets to close; for the purpose of this experiment, this functionality will more than likely not be used. However it does Figure 7 – Scitos G5 (Lincoln Centre for Autonomous Systems, 2015)
  • 27. Alyxander David May Page | 20 improve the safety aspect of the experiment. The controller of the experiment also has an overriding controller for the robot, so that if unexpected or bizarre behaviour occurs it can be remotely controlled and moved. 4.2 Development Methodology 4.2.1 Incremental Development The software development, has three main aspects indicate, move head and no signal. Thus it makes sense to deal with these individually, by developing one, testing and refining it before moving on to the next aspect. A good way to deal with this is using incremental development. Figure 9 - Waterfall vs Incremental (Bittner, 2006) Figure 9 shows an overview of the waterfall and iterative process, waterfall is described as ‘Taking an extreme waterfall approach means that you complete a number of phases in a strictly ordered sequence: requirements analysis, design, implementation/integration, and then testing.’ By Kroll of IBM (Kroll, 2004). The strictly ordered sequence is useful to the needs of this project, however agile principles of recurring development would also be useful. Using the incremental development style, the waterfall process can be followed for each of the different behaviours, i.e. design, code, integrate and test no signal, before completing the same process for indicators and then move head. By implementing this methodology, it gives the rigidness of waterfall to ensure that each behaviours has been implemented correctly and tested with
  • 28. Alyxander David May Page | 21 enough time before the experiment. However, by using incremental principles, it allows the requirements to change, i.e. if it is found during the development phase that there may be an issue implementing one of the three current behaviours, a new behaviour could be designed and implemented in time for the experiment and thus not allowing the time frames to be too drastically altered. 4.2.2 Action Lib The action lib stack, will be the main way in which the system will publish new goals to move base using ROS. Two scripts are required as can be seen in Figure 10, one to act as the client that sends the goals and the other a server that deals with the publishing of the goals. Figure 10 - Client-Server Interaction (ROS, 2015) Before any scripts are created, the action, feedback and results messages for the server must be made, all of these will be Pose Stamped messages, containing a status, map, position and orientation for the robot etc. The server will be responsible for sending goals and receiving feedback from move base and will publish results back to the client. Once the server receives the goal, the goal will be published to move base, the server will then wait for move base to be within 70cm of the published goal (using the Euclidean distance d(a, b) = √(a1 – b1)2 + (a2 – b2)2 ), or if the goal has been completely reached. Once the threshold has been met, the client will be informed and the current position of the robot be sent back. The client is where most of the functionality takes places. The first aspect is to check what number participant the client last dealt with, found by opening a CSV files containing the number of the last participant. Following this, the relevant CSV
  • 29. Alyxander David May Page | 22 file for the participant is opened, containing the list of behaviours the robot will exhibit in a randomized order. Each behaviour will have a related method in the client file, this will contain all of the movement commands, and signal commands as well as a wait at the end of behaviour, and this allows the participant to be back in place before the next behaviour starts, which is controlled by the experiment controller. Figure 11 - ROS Topic Graph Figure 11 shows the ROS Topics used by the implemented code. move_server first receives a goal from move_client then publishes it to move_base. move_base then waits for a result from move_base before sending the result back to move_client. move_server can also give feedback to move_client as to where the robot is. move_client can also cancel the goal using move_server/cancel. Finally the orientation is set by move_client using the DWAPlannerROS/parameter_update dynamic reconfigure. The indicators are powered by two of 5V ports on the robots PCB, to blink the indicators the ports need to be reconfigured on the fly to power on and off. This is done using the dynamic reconfigure (ROS, 2015) client, which allows a node’s parameter to be changed without having to restart the node. Dynamic reconfigure is also used to stop the robot attempting to set perfect orientation when it is moving between the goals, by setting the orientation tolerance to 2π (360o). Finally, the rate at 2Hz is controlled on a separate thread in which a sleep of 2Hz is applied before turning on and off the indicator respectively. The second behaviour, move head is controlled by sending a joint state message to topic head command, this allows a pan and tilt value to be sent and reposition the head into its respective position. 4.2.3 Testing Testing was completed using two different mediums, 3D simulation environment Blender and the robot. By having a simulated testing environment, it ensure that any small changes are able to be tested quickly and easily, also useful for when the robot was unavailable. Some testing will take place on a real robot
  • 30. Alyxander David May Page | 23 close to the time to the user study to ensure it is working as expect on real hardware as well as simulated. All the testing completed was regarded as black box testing, due to the software having nothing in the way of inputs, the test was simply to test and ensure functionality of the robot was safe and got to the correct places. 5 Experiment The main aspect of the experiment is to explore how various navigational signals impact the way a human responds both physically and mentally to an oncoming robot in a corridor passing scenario. The first aspect is to design and implementation of a suitable and feasible experiment. Following this conclusions will be drawn from the results of the experiment, as well as a discussion about how the experiment was executed. 5.1 Design 5.1.1 Physical The experiment was set up in Lincoln Centre for Autonomous Systems (L- CAS), part of the University of Lincoln. The robot was position at one end of the room 8 meters away from the participant facing one another, for each test the start locations were the same. Participants were asked to cross the room and arrive at the start location of the robot with in a manner they felt comfortable with, however with the caveat that they must start walking directly towards the robot. At the same time the robot would move in the opposing direction, thus the robot and participant were on a head-on collision path. Each participant completed seven different test, each with a different behaviour. 4/7 test included signals of navigational intent (see Figure 12 and Figure 13 for signals).
  • 31. Alyxander David May Page | 24 5.1.2 Robot Setup The robot could display three signals of navigational intention: no signal, indicate and move head. No signal, involved the robot moving between the points without any visual signals. For indicate, 5V LED amber indicators installed (Figure 13) on either side of the “body” just below the “head”, when the indicators were active, they would flash at a rate of 2Hz. Finally, move head involved moving the “head” 35o to the left or right dependant on the behaviour needed (Figure 12). The robot started each behaviour on a keyboard input, for the straight behaviour, the robot moved 7.5m forward, and this is the only test in which the human would actively have to circumnavigate the robot without a “collision”. For all other behaviours the robot started by moving 1.5m forward before starting to signal, with Figure 13 – Indicate Behaviour: Left, Straight, Right Figure 12 – Move Head Behaviour: left, Straight, Right
  • 32. Alyxander David May Page | 25 the exceptions of no signal, then a further 1m forward, followed by a 1.8m diagonal movement (1m sideways and 1.5m forward), the signal was “reset” after this. Next the robot moved 3m forward before reaching its terminal destination. This means the robot could take three different paths, regardless of signal with the same start position but different termination positions (Figure 14 shows available robot paths). In total there are seven independent behaviours the robot can use and each will be used on each participant in a randomized order. The behaviours are: move left, move right, move straight, move left with head, move right with head, move left with indicator and move right with indicator. It is important to note that when the robot exhibited the various behaviours the robot indicated its own navigational intent direction and it was not a command to the human. As well as this the robot did not deceive the human in any of the behaviours i.e. it never indicated left and then moved right. 5.1.3 Participants Ten participants completed the experiment, although none had any experience or idea of the nature of the experiment beforehand, half of the participants had previous contact that day with the robot by participating in a separate experiment. Each participant was issued with a general demographics form and a consent form to read through and fill out before starting the experiment (items A and B in appendices). Alongside the forms, the participants were verbally informed that they had the right to withdraw at any point including after the experiment and all data will be removed, and that all of their data was given anonymously and couldn’t be traced back to them. The participants were also informed they could ask questions before or after the experiment but not during as to keep the integrity of the experiment. Figure 14 - Available Robot Paths
  • 33. Alyxander David May Page | 26 At the start of the experiment each participant was informed they were to stand in the starting position and wait for the controller to instruct them to move. Once they were instructed the test had begun, they were to walk casually towards the robot and to navigate to the start point of the robot in as casual and natural manner as possible. The participants were also told that although the robot would not collide with them, its behaviour was not reactive to how they acted. After the participant had completed all seven of the behaviours they were issued with a survey form to fill in about their experience with the robot (item C in appendices). The experiment was then explained, including what the purpose of the research was, why it was being done, and what it was hoped the experiment would show. Finally, any questions they had could be asked and answered before thanking them for their time and reminding them of their rights. 5.2 Results With ten participants completing the experiment, seventy unique data sets were attained. The results from the survey are tabulated according to behaviour exhibited and question, these can be seen below in Figure 15. The behaviours are No Signal is NS, Indicator is I and Move Head is MH. The questions asked were: 1) “I was able to understand the intention of the robot when using this behaviour.” 2) “I felt comfortable passing the robot while it was exhibiting this behaviour.” 3) “I was quickly able to understand the intention of the robot.” Questions were answered using a Likert Scale: 1 - Strongly Disagree, 2 - Disagree, 3 - Neutral, 4 - Agree and 5 - Strongly Agree. Participant NS 1 NS 2 NS 3 I 1 I 2 I 3 MH 1 MH 2 MH3 1 2 3 2 4 4 3 4 4 4 2 2 2 2 4 4 5 4 3 4 3 1 2 4 5 4 5 3 2 4 4 2 3 1 5 4 5 4 4 4 5 2 2 3 4 2 4 4 5 4 6 3 4 2 5 5 5 4 4 3
  • 34. Alyxander David May Page | 27 7 3 4 3 4 4 4 2 4 3 8 4 3 4 5 4 5 2 3 1 9 3 2 4 4 4 4 3 3 3 10 1 1 1 4 5 4 5 5 5 Figure 15 - Survey Results As well as this, the robot tracked the position of the person, in 3D space, using itself as the origin. For the interest of this experiment only the X and Y axis are of need as they remained on the same height. The exact distance from the robot will be used, using simple Pythagoras 𝑎2 + 𝑏2 = 𝑐2 . The area of interest will be using a field of view from 1 3 4 to 1 4 π, with π directly behind the robot and 0 directly in front, the area of interest can be seen in Figure 16. Figure 16 - Robot Area of Interest for Minimum Distances Figure 17 shows minimum distances kept each test for each participant, unit used is meters. Participant Straight Left Right Indicate Left Indicate Right Head Left Head Right P1 0.72647 0.75379 1.29989 1.62642 1.00610 1.16366 1.15255 P2 1.54390 1.50569 1.53723 1.61397 1.59526 1.19771 0.73154 P3 0.67134 0.57304 0.90999 1.68446 1.31759 0.69555 0.52814 P4 0.72808 0.64711 0.85181 1.35578 1.34077 1.02913 1.14908
  • 35. Alyxander David May Page | 28 P5 2.41629 1.60650 1.33606 2.59011 2.44219 0.65425 1.13071 P6 1.11463 1.05780 1.29263 1.31898 2.17899 1.52792 2.16402 P7 1.25423 1.31454 1.42037 1.77986 1.92819 0.65792 1.07275 P8 0.61908 0.48311 0.48145 1.74640 1.04203 1.24667 0.78165 P9 0.74319 0.52994 0.33715 0.85010 0.86872 1.50603 1.70050 P10 0.47898 0.72195 0.99406 1.03909 0.99406 1.65893 1.06612 Mean 1.02962 0.91935 1.04606 1.56052 1.47139 1.13378 1.14771 Figure 17 - Minimum Distance from Robot (m) 5.3 Evaluation 5.3.1 Questionnaire Sections 1, 2 & 3 The first aspect of the evaluation is to look at the results of the surveys, the results to the question 1, 2 and 3 for each behaviour can be seen below in Figures 18, 20 and 22, including standard error of mean error bars and the results of paired t-test between No Signal and Indicators. Figure 18 - Results Question 1 The first question looks at how well the participants could understand what the robot was intending to do in each of the behaviours. Just by looking at the graphs, indicators were most understandable, with the highest mean and lowest standard error of mean, with the mean being 4.33 ± 0.17 and mode of 4. The second
  • 36. Alyxander David May Page | 29 most understood was the move head movement behaviour, with a mean of 3.44 ± 0.27 and mode of 4. Finally the least understood behaviour was no signal, with a mean of 2.22 ± 0.28 and mode of 2.  There was extreme significant difference in the scores for Indicators (M = 4.33, SD = 0.52) and No Signal (M = 2.22, SD = 0.95) with conditions; t (9) = 6.68, p = >0.0001  There was not quite a statistical difference in the scores for Move Head (M = 3.44, SD = 0.97) and No Signal (M = 2.22, SD = 0.95) with conditions; t (9) = 2.17, p = 0.0584  There was a statistical significant difference in the scores for Indicators (M = 4.33, SD = 0.52) and Move Head (M = 3.44, SD = 0.97); with conditions t (9) = 2.38, p = 0.0414 Figure 19 - Score Differences for Question 1 These results suggest that indicators do affect humans understanding the navigational intent of a robot compared to no signal and head movement this can be seen in Figure 19 showing the score difference between no signal and indicators for each participant. However there was there was no significant affect on the understanding between head movement and no signal. Specifically, these results suggest that robots using indicators to display navigational intent are more understood by humans than those using a head movement or no singal, and robots 0 1 2 3 4 5 NO S IGNA L INDIC A T ORS PARTICIPANT SCORES DIFFERENCE FOR QUESTION 1
  • 37. Alyxander David May Page | 30 using a head movement to display navigational intent are not significantly more understood that those using no signal. Figure 20 - Results Question 2 The second questions looks at how comfortable the participants felt when passing the robot in each of the behaviours. Again participants selected indicators as the most comfortable behaviour exhibited with the lowest standard error of the mean; at mean 3.89 ± 0.24 mode of 4. Followed by closely by move head behaviour, with a mean of 3.67 ± 0.28 mode of 4. Finally, no signal is lowest of the behaviours, with mean 2.44 ± 0.35 mode of 2.  There was a very statistically significant difference in the scores for Indicators (M = 3.89, SD = 0.82) and No Signal (M = 2.44, SD = 0.97) with the conditions; t (9) = 3.77, p = 0.0044  There was no statistical significance in the scores for Indicators (M = 3.89, SD = 0.82) and Move Head (M = 3.67, SD = 0.95) with the conditions; t (9) = 0.71, p = 0.4961  There was a statistical significance in the scores for Move Head (M = 3.67, SD = 0.95) and No Signal (M = 2.44, SD = 0.97) with the conditions; t (9) = 2.54, p = 0.0318
  • 38. Alyxander David May Page | 31 Figure 21 - Score Differences for Question 2 These results suggest that indicators and head movement do affect humans comfort when passing a robot compared to using no signal this can be seen in Figure 21 showing the score difference between no signal and indicators for each participant. However there was no statistical significance in the affect for using indicator compared to head movement. Specifically, these results suggest that when a robot is display navigation intent with either indicators of a head movement humans are feel more comfortable passing the robot then not displaying any signal, also humans do not feel significantly more comfortable passing a robot using indicates instead of head movement. 0 1 2 3 4 5 NO S IGNA L INDIC A T ORS PARTICIPANT SCORES DIFFERENCE FOR QUESTION 2
  • 39. Alyxander David May Page | 32 Figure 22 - Results Question 3 The third question looks at how quickly people could understand the intention of the robot, participants also selected indicators as the quickest to understand the intention of the robot; with a mean of 4.33 ± 0.23 mode of 5. Second again was move head with a mean of 3.56 ± 0.34 mode of 4 and finally no signal with a mean of 2.67 ± 0.35 mode of 2.  There was a very statistically significant difference in the scores between Indicators (M = 4.33, SD = 0.70) and No Signal (M = 3.56, SD = 1.17) with the conditions; t (9) = 4.32, p = 0.0019  There was not quite a statically significant difference in the scores between Indicators (M = 4.33, SD = 0.70) and Move Head (M = 3.56, SD = 1.08) with the condition; t (9) = 1.96, p = 0.0811  There was not a statistically significant difference in the scores between Move Head (M = 3.56, SD = 1.08) and No Signal (M = 3.56, SD = 1.17) with the condition; t (9) = 1.41, p = 0.1934
  • 40. Alyxander David May Page | 33 Figure 23 - Score Differences for Question 3 These results suggest that indicators do affect how quickly humans can understand the navigational intent of a robot compared to using no signal or head movement signal this can be seen in Figure 23 showing the score difference between no signal and indicators for each participant. However, there was no significant difference between how quickly the robot was understood between head movement and no signal or indicators and move head. Specifically, these results suggest that humans are able to understand the navigational intent of a robot using indicators instead of no signal significantly quicker. Finally, the results also suggest that humans aren’t able to understand navigational intent significantly quicker using head movement instead of no signal or using indicators instead of head movement. 5.3.2 Questionnaire Section 4 The first question of this section was asking which of the behaviours the participants would most like to see implemented as standardized behaviour for robots displaying navigational intent. Six people answered with indicators and four with head movement. However, all of the participants who listed their experience with robotics as little or none also listed head movement, a few stating “as it is the most natural”, whereas all who listed their experience with robotics as some or more all choose indicators with statements such as “indicators as it is a well know framework for changing and could have the most public understanding” or 0 1 2 3 4 5 NO S IGNA L S P E E D INDIC A T ORS S P E E D PARTICPANT SCORES DIFFERENCE FOR QUESTION 3
  • 41. Alyxander David May Page | 34 “indicators as they can be seen at a glance”. The clear split in opinion dependant on experience with robotics is an interesting point that arose, the experience with technology however, seemed to have no over bearing factor on the response to the question and similar to that of the work by Hall et al. who found a bias in peoples comfort with robots will self-confessed experience (Hall, et al., 2014). Second question asked if the participants could think of an alternative signal of navigational intent they would prefer to those trailed in the experiment. Two of the participants stated they would like to have a verbal signal as well as visual, one stating “… also more accessible to the visually impaired”. One participant stated “have a standard side, like on the UK roads (robots could) pass on the left”. Lastly one person stated “(have a) small sideways movement before turning”. The point of this experiment was to look into non-verbal was of expressing navigational intent, but a verbal and non-verbal method could be used, and two of the participants feel this would be beneficial, this is similar to the finding of Peters et al. who found some participants would have like a verbal signal instead (Peters, et al., 2011). The response about passing on one side is an interesting notion and not one that was thought of for this experiment, it would however require a consensus in a large population to be useful, also for the purpose of this experiment it wouldn’t have been used as it would have required explaining more about the experiment to participants. Finally, a small sideways notion again wasn’t consider in the experiment, but could be researched, a conversation with the participant after concluded that it wouldn’t be an ideal solution as it isn’t “obvious” enough in that people may not notice it they were distracted. The final question asked if any additional actions by the robot was noted in any of the trails. Three participants provided an answer to this question: “sometimes the robot reacted quicker to my presence than in others”, “she (Linda the robot) went too far in one direction to avoid me” and “it felt like she followed me for some time on the 6th test”. For the first two questions, it was explained to the participants after the experiment that the robots behaviour was not reactive and the only time it would become reactive was if they got extremely close to the robot, it would attempt to circumnavigate them rather than follow its path. This happened for the final answer, the robot went right (using no signal) and the participant did the same, once the robot got two close it tried to plan a new goal and went around 360o on the spot the
  • 42. Alyxander David May Page | 35 same direction as the participant, giving the perception they were being followed. After these behaviours were explained, the participants had no additional points to bring up. 5.3.3 Robot Data Figure 24 - Average Minimum Distances For the mean distances, each behaviour has been looked at rather than each test i.e. Indicators is indicate left and right etc. this way biased can be lower of a person circumnavigating further to the left than the right for any reason. The results show that participants kept furthest away from the robot during the indicate behaviour with a mean distance of 1.52m ± 0.11, flowed by the head at 1.14m ± 0.09 and finally no signal at 0.98m ± 0.09. The straight behaviour was also include in the experiment, but wasn’t include in any of the behaviours, the average distance kept was 1.02m ± 0.13. T tests were performed on the corresponding data, with left and right tested against each other e.g. head left & head right and indicate left & indicate right but will be listed as move head and indicate.
  • 43. Alyxander David May Page | 36  There was an extremely statistically significant difference in the distances between Indicators (M = 1.52, SD = 0.50) and No Signal (M = 0.98, SD = 0.41) with the conditions; t (19) = 5.78, p = >0.0001  There was a statistically significant difference in the distances between Indicators (M = 1.52, SD = 0.50) and Move Head (M = 1.14, SD = 0.42) with the condition; t (19) = 2.38, p = 0.0277  There was no statistically significant difference in the distances between Move Head (M = 1.14, SD = 0.42) and No Signal (M = 0.98, SD = 0.41) with the conditions; t (19) = 1.11, p = 0.2801 These results suggest that indicators do affect significantly how close humans get to a robot while passing, compared to using no signal and head movement, however using a head movement instead of no signal there is no statistical significance. Specifically, these results suggest that humans will pass further away from the robot when it uses indicators to signal navigation intent compared to no signal or head movement, and that passing distance is not statistically different comparing head movement and no signal. Figure 25 - Behaviour Distribution Figure 25 shows a box and whiskers diagram for the minimum distances for each behaviour. It can be seen that the ranges of Move Head and No Signal are
  • 44. Alyxander David May Page | 37 very closely related, with a slightly high range for move head, the interquartile range (IQR) is much the same with a slightly more contained IQR for No Signal slightly more contained, both have a similar medium. On the other hand, the plot for Indicators is quite different, the is similar, however, with a significantly higher maximum and lower minimum compared to move head and no signal, the same can be said about the IQR which is again much the same size but shifted to the right. However Q3 and maximum are much further from the medium for indicators, showing there is a large spread in the larger 50% of the data compared to the smaller 25%. 5.3.4 Summary The hypotheses previously stated were: 1) Humans feel more comfortable and are able to quickly and correctly understand the intention of a robot when using indicators to express navigational intention than no signal The new empirical data gained supports this hypothesis. There is statistically significant difference in the comfort humans feel as well as the speed and ease they understand the navigational intention of a mobile robot using indicators compared to no signal. It also appears to be a positive difference, in the mean a mode scores for indicators for each question being higher than that of no signal. 2) Humans feel more comfortable and are able to quickly and correctly understand the intention of a robot when using its head to express navigational intention than no signal The data gained, neither supports nor disproves this hypothesis. There is a statistically significant difference in the comfort felt for participants between move head and no signal, which appears to be in favour of more comfort felt during the move head behaviour due to the higher mean and mode score. On the other hand, there is no significant difference in the scores for the t-test between the move head and no signal behaviours, this trend continues into the mean scores, although the no signal mean is lower for both questions, it is a negligible amount, unlike the mode which again remains higher with two points more.
  • 45. Alyxander David May Page | 38 3) Humans will move further away from the robot when it is indicating navigational intent than when using no signal The experiment data supports this hypothesis, the t-test value shows significant differences in the distances kept from the robot to in the behaviours of indicators and no signal. Specifically, the data shows that humans kept a greater average distance from the robot when using indicators to show navigational intent, the mean is significantly greater for indicators as. Furthermore, the box and whisker plots shows that the spread is shifted to the right with a larger upper range of minimum distances kept during the indicate behaviour. 4) Humans will move further away from the robot when it is using its head to express navigational intent than when using no signal For the final hypothesis, the data again neither supports nor refutes the hypothesis. There is no significant difference in the minimum distance kept between no signal and move head. The means again are very similar, although the mean for no signal is lower again, the difference is not a significant one. The box and whiskers diagram, shows how similar the data really is, with near on a carbon copy for the ranges, medium and interquartile range. Following on from the experiment it is suggested that the favoured behaviour for: ease of understanding, speed of understanding and comfort felt of is indictors, followed by move head and lastly no signal; which appears to conform with the findings of Peters et al. although they didn’t implement the indicators a large amount of their user group suggested them (Peters, et al., 2011). This is based on the mean statistics for the post experiment survey. The order is also the same for the minimum distances kept, with participants staying furthest away while the robot was indicating followed by moving head and lastly no signal. 5.4 Limitations One limitations during the research is the size of the experiment, with only ten participants only seventy new sets of data have been acquired. As well as the experiment missing depth of participants it is also missing breath, with 9/10 participants being male and again 9/10 being students, taken as a pilot experiment interesting information has been acquired and is appears as a good starting point
  • 46. Alyxander David May Page | 39 for further research. Furthermore, half of the participants had previous contact with the used robot that same day, this may influence how comfortable the person felt around the robot if that had previously contact that day. However, the contact was not related to the purpose of this experiment, although half of the participants had previous contact with the robot, they had no additional information on the nature, purpose or requirements of this experiment, but again with the statement of Hall et al. this experience may be enough, more so with using the same robot to cause bias (Hall, et al., 2014). A major limitation is that some key aspects of comfort in HRSI as defined by Kruse et al., including speed and acceleration are not considered and left constant. Comfort is a very specific feeling and has been shown to be greatly be affected by various factors, whilst trying to keep as many factors constant as possible, it may have had a negative effect in that one behaviour may be preferred at the current speed or acceleration used rather than in general (Kruse, et al., 2012). The same can be said for the robot used in the experiment, using another robot the effects of various behaviours may have had a different effect on the comfort felt by the participants. Another limitation stems from the hardware of the actual robot and its positioning. The perception and understanding may be effected from the positioning of the hardware and the way it signals. To remove as much biased as possible the indicators were made as “standard” as possible; this was achieved by using similar size indicators to that of a motorcycle, at a rate of 2Hz using an amber light. The head behaviour only rotates the internal of the head; again to attempt to remove biased, the indicators were mounted as close to the head as possible, as not to cause people to only look for indicators. Before the experiment, participants were instructed to change positions with the robot in as “casual and natural manner” as possible. This statement along with the setup of the laboratory may have led to difference in the way participants interacted with the robot in terms of naturalness of interaction in a given direction. However, looking at Figure 10, the average distances kept from the robot during the left and right trails for each behaviours, aren’t significantly different. A similar limitation was noted by Dondrup et al., however their conclusion were also that the
  • 47. Alyxander David May Page | 40 message to participants before the start of the study showed a negligible effect on results (Dondrup, et al., 2015). One major limitation came from the speed at which some participants completed the first trail in their test set, due unfamiliarity with the robot. On two occasions the participant had walked with such velocity that they had passed the robot before it had made its navigational movement. On the other hand there were occasions when participants walked with so little velocity that the robot had completed its path change before the participant was near the robot. However, the order of the trails were randomized, so this should have mo major swing on one particular test set. Finally, a large part of the data is based on information from the questionnaire, of which a Likert Scale is used. Using a Likert scale it is important to remember that the difference between each value may not be equally weighted in the views of the user. E.g. is the difference between ‘Strongly Agree’ and ‘Agree’ the same difference between ‘Neutral’ and ‘Disagree’. However the Likert scale is a commonly used medium that many participants would have seen at some point before. The use of a five point scale also reduces the amount of swing that people can feel compared to a larger scale. 5.5 Future Work The first extension to this work would include attempting the experiment again, with a more refined testing strategy as learnt from this iteration. The experiment would need to contain a significant increase in participation, thus allowing for a greater confidence in any results gained. Another requirement would be a more diverse participant group, with specific interest regarding variance in self-defined robotics knowledge. Another possible extension to the project could be the implementation of addition signal(s). This work looks at using three signals, extensions could include looking at using different signals. One could be using a screen with images of an arrow pointing in the direction of intended direction. A second as learnt from the user survey could be the addition of an audio signal, this has the potential to open many paths e.g. what is the most preferable audio signal (also suggested in study by Peters et al.) “I’m going left now”, “please go left human” etc. or the possibility of blending with a
  • 48. Alyxander David May Page | 41 non-verbal method such as adding clicking to indicator or a simulated loud motor noise to show turning (Peters, et al., 2011). A third option could be a sideways movement before then intended path change, again similar to that implemented by Peters et al. Similar to the work of Pacchierotti et al. speed, acceleration and passing distances could be experimented with alongside the indicators or head movement possibly using a stationary participant (Pacchierotti, et al., 2005). Part of this stems from the results of the minimum distances kept, when using the indicators humans kept further away from the robot, it would be interesting if they expect the robot to also circumnavigate at a further distance to feel most comfortable whilst using indicators, as well as the speed and acceleration used during the experiment. This work could be mixed with that of Lichtenthäle et al. to look at navigational signals during a crossover scenarior rather than a pass by. (Lichtenthäler, et al., 2013). Similarly, if the experiment took place in a real world environment the factors would be incredibly different and more natural. The experiment by Dondrup et al. gave a scenario of the participants and the robot waiting tables and passing each other in corridors (Dondrup, et al., 2014). Although the experiment was implemented in a laboratory, either the principle of setting the participants a task, or using a real world experiment in a restaurant could be used. These could be options to gain “real world” experience and review the implantations in a more stressful set of circumstances to see if the results are the same. A mixture of these two project experiments could also be used, in terms of a real world path crossing experiment. Finally, using principles from the second experiment by Dondrup et al. varying distances could be used for the signals (Dondrup, et al., 2015). Although indicators was favourable in terms of comfort in this experiment, it can’t be deduced that at varying distances this would still be the preferred behaviour. Looking at varying distances will allow a stronger conclusion to this experiment.
  • 49. Alyxander David May Page | 42 5.6 Conclusion The objectives for this project were: 1) Continue literature review to identify three methods of expressing navigational intent that are legible, safe and effective, as well as formation of hypotheses for later testing The completion of this objective can be seen in Section 2, as well as the three navigational signals spoken about during the course of the experiment: No Signal, Indicate and Move Head. 2) Implement chosen methods using ROS in Python and testing for reliability using either robot or a simulation environment The source code for the methods can be seen in on the included CD with the report or available on at: https://github.com/LCAS/navigation_intention. 3) Create and run an experiment to test human perception for each implementation, with appropriate post-experiment survey for data gathering The experiment was designed and run successfully, all aspects of the experiment can be seen in Section 5. The survey forms can be found in Appendix item a, b and c. 4) Use appropriate quantitative and qualitative methods to review data acquired from the experiment and test against hypotheses The data gathered was analysed, reviewed and tested against the hypotheses, these result scan be seen in Sections 5.3. The aim of this work was gauge how much HRI can be enhanced by implementing navigational signals. This work implemented three method of displaying navigation intent, to investigate how signals of navigational intent affect the comfort felt by people around social robots as well as the speed and ease of understanding these signals. The data from the experiments supplies strong evidence to support navigational signals helping benefit HRSI. The results show comprehensive evidence to support humans comfort, speed and ease of understanding indicators as a mode of navigational intent compare to that of no signal as well as minor positive differences between head movement over no signal. Thus it would be argued that navigational signals can have a significant effect on comfort felt for humans around mobile service robot.
  • 50. Alyxander David May Page | 43 The work attempted to look at one of the key challenges in social robotics as defined by Kruse et al. in their survey on Human-Aware Robot Navigation; Comfort (Kruse, et al., 2013). The experiment found that of three implemented behaviours, No Signal, Indicators and Move Head; Indicators were most preferable. As a side consequence the study also found data regarding at another challenge defined by Kruse et al.; Naturalness. Four of the ten participants that took part in the trails listed the behaviour they wished to be made a standard for social robotics, due to it feeling most “natural” to them. 6 Personal Reflection At the start of this year I knew nothing about ROS and little about Python or Ethical and Empirical research. I’ve become a more independent, self-directed and self-motivated learner, however I’ve also learnt to ask for support if I need it. Finally, although being an independent project, I’ve felt part of a team with my supervisor Marc, Christian and all those doing projects as part of the STRANDS. I was unsure what to do as my Masters Project and after speaking to Marc I was fairly sure I wouldn’t have the skillset required to undertake this project. However with his and Christian’s drive, enthusiasm, wisdom and support I feel more confident than ever. The start of the project for myself was easiest, I know how to write a proposal, and the theory and psychology behind what I was doing and why was clear to me. Thus for this part I was happy to continue fairly independently checking in with Marc and Christian ever two weeks at our meetings. Once the practical part began, I carried on independently. This could have meant the failure of this project; while the support and guidance I got during scheduled meetings was invaluable, instead of asking questions and checking in in-between meetings I carried on independently; I had questions I just waited until the meetings to ask. Thankfully I became more confident in asking question and the fact that I wouldn’t be able to continue the way I was. A key principle this work has shown me is that it is okay to ask for help when needed and people are a lot more responsive to it rather than waiting for weeks before asking. Personally, I believe the project went well and I am proud of the work I produced. I’ve shown to myself the ability to quickly pick up a new programming
  • 51. Alyxander David May Page | 44 language, operating system and middleware, in the future I will be able to refer to how flexible I am in project work using new systems thanks to this project. I believe the implementation of the navigation signals are useful, novel and intuitive and they led to an excellent overall display of the robot. For myself the experiment was where I gained the most experience, I’ve participated, but never run an experiment before. I got fantastic support from my colleges on how to setup and run an ethical experiment. In my opinion the setup was clear, concise, intuitive and interesting. I believe the participants were treated ethically and respectfully, as well as being fully aware of their rights. Personally, I have learnt the difficulty, stress, time and pressure it takes to setup and source participants in an experiment. The experience I have gained from this will help me if I chose to continue my interest in social robotics. I also feel honoured with the level of trust shown in me, with being shown how to control the robot for the experiment, and to record the data I needed, then being allowed to continue my experiments unsupported. In September, I had zero idea of what I wanted to do after this year of University, whether I would get a graduate job, attempt to continue in academia or go back to working with the disabled. This year has shown me a way I could do aspects of these together. I’ve gained skills I never thought I would have and totally enjoyed myself whilst doing it. I would like to finish by saying social robotics is fun.
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