This document discusses potential applications and configurations of coordinated systems using unmanned aerial vehicles (UAVs) and automated guided vehicles (AGVs). It first defines UAVs, AGVs, and mobile robot manipulators (MRMs). It then discusses the need for UAV-AGV collaboration to overcome individual limitations and complete tasks more efficiently. Various roles for UAVs and AGVs are described including sensors, actuators, decision makers, and auxiliary facilities. Examples of applications and configurations are provided where UAVs and AGVs take on different functional roles. Modes of coordination like perception, planning, and motion are also summarized. Finally, challenges like task modeling, dynamic role allocation, computational complexity, and scalability are

1. Literature Review on Application and Usage
Potential for Combination of UAV and AGV

2. What are AGV, MRM and UAV?
AGV(Automated Guided
Vehicle)
Mobile Robot carry heavy
loads from one place to
another
UAV(Unmanned Aerial
Vehicle)
Aerial Robot generally used for
surveying and monitoring
purpose
MRM(Mobile Robot
Manipulator)
Jannis S et al. (2022) defined MRM
as combination of AGV and
Manipulator designed for specific
task

3. Need for Collaboration…..
• UAV cannot carry large battery back up
and AGV have less speed
Different constraint for
both UAV and AGV
• UAV is not good in Indoor Application while
AGV is not good in rough terrain
Limitation of task
performance by single
robot
• Combining UAV and AGV for a task will reduce
the power consumption of one vehicle
Less Power
Consumption
• Co-ordinating both vehicle will reduce the time
consumption for particular task
Reduced Time and
Labour

4. Functional Role in UAV-AGV Co-ordination System
Yulong Ding et.al. (2020) categorize the
UAV and AGV in different functional role
as :
• Sensors detects the target or change in
the environment and send it to other
components or vehicle.
• Actuators perform the action.
• Decision Makers makes decisions like
path planning, motion control etc. for
other components or vehicle.
• Auxiliary Facilities provides main
components energy, communication,
computation and other services.
SENSORS
DECISION
MAKERS
ACTUATORS
AUXILIARY
FACILITIES

5. Applications of UAV AGV Co-ordinate System
Combined
AGV - UAV
Different Role
Navigation
in GPS
challenged
environme
nt
Large-scale
collaboration
by UAV
providing
communicati
on
Large scale
exploration,
mapping and
surveillance
Accurate
Detection
for target
Same Role
Data
Collection,
Localization
and
navigation
Target
tracking
Team
Formation

6. UAV acting as Actuator and AGV as Auxiliary Facility
AGV as Mobile Carrier
UAV Landing on AGV
• Rodriguez et al. (2018) Vision
Based Autonomous Landing
• Fu et al. (2016) gives GPS based
navigation algorithm for
autonomous landing.
UAV for Package Delivery
• S. M. Ferrandez et al. (2016) AGV
reached the desired location and
UAV will deliver the package
AGV as Mobile Reference Station
Jung et al. (2016) proposes AGV
act as Mobile differential Global
Navigation Satellite System
reference station which reduce the
navigation uncertainty of UAV
Sivaneri et al. (2017) While moving
from outdoor to indoor or vice
versa for site inspection, structural
health monitoring and surveying

7. • AGV can offset the UAV’s Flight time disadvantage and enable it to collect data in a
very large area, Tokekar et al. (2016) , Ropero et al.(2019) and Liu et al. (2019)
suggested such system.
• AGV-UAV coordination deployed in precision agriculture, target surveillance and
power line inspection
UAV act as Sensor and AGV
as Auxiliary Facility
• Stentz et al. (2003) has UAV collect and transmit data and AGV plan it’s path
according to received information
• Gathering information about the surrounding environment and target will improve
the efficiency of the planning for AGV, Peterson et al.(2018), and Arthur et al.(2005)
gives such systems.
UAV act as Sensor and AGV
as Actuator
• Michael et al. (2008) UAV provide environmental information as well as monitor and
guide the AGV.
• UAV uses multiple cameras and vision based control method for driving a set of AGV
to desired formation Rao et al.(2003) and Mathews et al. (2019) introduces such
system.
UAV act as Sensors as well
as Decision Makers and
AGV as Actuators
• UAV provide communication to the AGV
• UAV hover at fixed location to provide coverage of area require communication or
to enhance coverage area Mozaffari et al. (2016) and Cheng et al. (2018)
UAV acting as Auxiliary
Facilities and AGV as
Actuators
Other Configurations……..

8. Modes of Coordination…..
• Gordon C et al. (2017) suggested two types of perception coordination:
• Complementary coordination where multiple information sources
supply different information about same feature.
• Cooperative coordination uses the information extracted by multiple
independent sensors to provide information which is not available by
single sensor
Perception
Coordination
• Point to point path planning to find optimal paths from start to target
configuration Yeifeng C et al. (2017)
• Coverage path planning to plan an optimal path that passes over all
points of an area or volume of interest.
• Multiple waypoint path planning to find the shortest possible route that
stops at each waypoint.
Planning and Decision
Making Coordination
• UAV-AGV move according to some constraint on the team as a whole
Abdullah M et al. (2020)
• Centralized Coordination where all computation and control are
performed in global central station
• Distributed Coordination requires no central controller and all
measures and controls are performed by individual
Motion Coordination

9. Challenges and Insights……..
Task Modeling and
Identification
• Modeling and
identifying the
task scenarios lies
at the root of
AGV-UAV
coordination
system
• In completing
complex tasks in
uncertain
environment , it
will be
automated as
conditions
change
Dynamic
Functional Role
Allocation
• UAV-AGV
coordination
system with
dynamic
functional roles.
• The assigned role
will consider the
trade off
between the
redundancy of
vehicle
capabilities and
fault tolerance or
robustness to
vehicle failure
Computational
Complexity
• System has
limited
computational
capability due to
size and weight
constraint like
bandwidth
scarcity, poor or
unreliable
connectivity and
minimum latency
requirement.
• More advanced
embedded
hardware
technology
should be
designed
Scalability and
heterogeneity
Trade-off
• System need to
be scalable and
adaptable with
dynamic
environment and
task complexity
• Developing
planning
algorithms to
strike task
dependent
balance between
scalability and
heterogeneity.
Human in the loop
• Interaction with
human.
• Human
interaction
improve the
performance and
management of
the system

10. References…..
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