A Survey Paper on Controlling Computer using Hand Gestures
Human Controlled Robotic Hand Capstone Project
1. FABRICATION OF HUMAN
CONTROLLED ROBOTIC HAND
CAPSTONE PROJECT
Submitted in Partial Fulfilment of the
Requirement for Award of the Degree
Of
BACHELOR OF TECHNOLOGY
In
MECHANICAL ENGINEERING
By
PRASHANT ANAND RANJAN (11207739)
AKSHAY KUMAR (11206819)
AKSHAY SAINI (11206909)
HITESH JYOTI (11206621)
Under the Guidance of
PUNEET KUMAR DAWER
DEPARTMENT OF MECHANICAL ENGINEERING
LOVELY PROFESSIONAL UNIVERSITY
PHAGWARA, PUNJAB (INDIA)-144411
2016
2. i
Lovely Professional University Jalandhar, Punjab
CERTIFICATE
I hereby certify that the work which is being presented in the
Capstone project/Dissertation entitled “Fabrication of Human
Controlled Robotic Hand” in partial fulfilment of the requirement for the
award of degree of Batchelor of technology and submitted in Department
of Mechanical Engineering, Lovely Professional University, Punjab is an
authentic record of my own work carried out during period of
Capstone/Dissertation under the supervision of Mr. Puneet Kumar
Dawer, Assistant Professor, Department of Mechanical Engineering, Lovely
Professional University, Punjab (India).
The matter presented in this dissertation has not been submitted by
anywhere for the award of any other degree or to any other institute.
Date:
Prashant Anand Ranjan, Akshay Kumar, Akshay Saini, Hitesh Jyoti
This is to certify that the above statement made by the candidate is
correct to best of my knowledge.
Date: Puneet Kumar Dawer
Supervisor
The B.Tech Capstone project of Fabrication of Human Controlled
Robotic Hand, has been held on 25th of May, 2016
3. ii
ACKNOWLEDGEMENT
In a humble expression we show our gratitude towards all those people
who supported us throughout this course of capstone project. Especially we
thank our mentor who stayed with us in the path of guidance since begin
till the end. We are also thankful for numerous retailers who provided us
with the accurate information about the parts of project keeping in mind
their utility in our project work.
We express our warm thanks to our H.O.S. Gurpreet Singh Phull,
C.O.S Ankur Bhal and our mentor Mr. Puneet Kumar Dawer for their support
and guidance.
Prashant Anand Ranjan (11207739)
Akshay Kumar (11206819)
Akshay Saini (11206909)
Hitesh Jyoti (11206621)
4. iii
ABSTRACT
Artificial robotic hand has become popular in world of robotics. Artificial
robotic hand is an electro-mechanical system whose working is similar to
human hand whose movement is instructed by a very small microcontroller
in response to the signals generated from the sensor. The microprocessor
brick capable of driving basically five servo motors design to form an
anthropomorphic structure. The concept used is that when the user gives
the input to microcontroller by changing the resistance with the help of
potentiometer a signal is being generated which gives the instruction to a
particular servo motor to move and hence give the movement to respective
finger. As soon as the response is being stopped the motor will be stopped
automatically and the finger will remain in the same position until more
response is given. The proposed design would enable the enthusiastic
manufacturing teams to improve their production involving innovation for
human kind. Moreover, more innovative structure can be designed using
the similar concept with more expenses using more advanced components
as there is a very vast degree of innovation possible in this project.
5. iv
TABLE OF CONTENTS
SR. No. Topic Page No.
I. Certificate i
II. Acknowledgement Ii
III. Abstract iii
1. Introduction 1
2. Literature Review
2.1 Contact Technologies
2.2 Non-contact Technologies
2
3
6
3. Components of Artificial Robotic Hand
3.1 Microcontroller
3.1.1 Overview
3.1.2 Technical Specification
3.1.3 Power
3.1.4 Memory
3.1.5 Schematic Diagram Input and Output
3.1.6 Communication
3.2 Servo Motor
3.2.1 Overview
3.2.2 Servo Working
3.3 Potentiometer
3.3.1 Working Principle of Potentiometer
10
11
11
12
13
13
13
14
14
14
15
15
17
4. Construction and Working of Artificial Robotic Hand 17
6. v
4.1 Testing
4.1.1 Virtual Testing
4.1.2 Real Time Testing
4.2 Construction
4.3 Working of Artificial Robotic Hand
4.3.1 Block Diagram
4.3.2 Working
4.3.3 Programming
21
5. Result, Conclusion and Future Scope
5.1 Result
5.2 Failure
5.3 Conclusion
5.4 Future Scope
26
26
26
26
27
Reference 28
21
23
23
24
24
24
25
7. vi
LIST OF FIGURES
SR No. Figure Page No.
2.1 Classification Of Hand Reconstruction Techniques 3
2.2 Types Of Controller 5
2.3 Position Sensor 6
3.1 Robotic Hand
(a)Rear View
(b)Inside View
11
3.2 Arduino UNO 11
3.3 Arduino Design 13
3.4 Servomotor (Tower Pro) 14
3.5 Servo Position 16
3.6 Potentiometer 17
3.7 Potential Difference across Potentiometer 17
3.8 Basic Working of Potentiometer 19
3.9 Detailed Working of Potentiometer 20
4.1 Virtual Testing Circuit (Proteus) 22
4.2 Real Testing Circuit (Breadboard) 23
4.3 Programming (Arduino) 25
8. 1
CHAPTER 1: INTRODUCTION
In this era of technological advancement where engineers are building
ingenious designs of robot to reduce human labour as much as they can by
combining the concept of nature or real world entities with technology to
copy there response with greater capability this concept of artificial robotic
arm is evolved with an attempt to fabricate human hand movement.
An artificial robotic hand is a robot manipulator which is programmable and
its functions are almost similar to that of human hand. The design is
basically the end effecter of a robotic manipulator and it is analogous to the
human hand. The end effecter is designed to perform any desired task such
as welding, gripping etc., depending on the application. It is controlled
manually and can be used to perform a variety of tasks
with great accuracy.
The device has its own built-in logic and all the movement of device is being
controlled by servomotors which form a vital part of robotic hand.
APPROACH:
We were able to perform a detailed study of the robotic hand and the micro-
controller. We tested the built robotic hand and the servo motors when the
robot is loaded. We also learnt and familiarized with the micro-controller
using assembly language, and converting the assembly language codes to
hexadecimal codes using a development board.
9. 2
CHAPTER 2: LITERATURE REVIEW
There is a great amount of work done in identifying the motion of the
human body and, in particular, of the human hand. The fields of interest
are also diverse; much of the work has been done in the area of computer
graphics, in order to create realistic virtual motion for avatar animation, for
automatic hand language identification, for automatic sketching, etc.; also
in the areas of humanoid robotics, in order to program human-like motion
in the robots, and in the area of biomechanics.
This diversity of goals led to many different techniques being developed.
Here we will try to compile the most successful ones and also adept some
general classification scheme.
The extraction of motion and posture information has been categorized by
Varga et al. according to three aspects, that we reproduce here as a good
framework to classify the previous and actual research. The extraction of
motion information can be categorized according to the sensing device used
as contact (in which the device is mounted on, or touches the hand) or non-
contact (in which the information is extracted at a distance). Regarding
whether the whole hand or only some characteristic points are tracked, the
information is classified as complete or incomplete.
This information can be transferred directly to some geometric modelling
system (direct transfer) or the information can be fed to an intermediate
hand model that adjusts the raw information before sending it to the
geometric modeller (indirect transfer, also called model-based). Figure 2.1
shows a graphic depicting those options. Notice that any combination may
give valid data; in the Figure, we have highlighted the options that may be
used in our research.
10. 3
Figure 2.1: Classification of hand reconstruction techniques [1]
2.1 Contact technologies
Contact devices [1] can be classified as data gloves (with different sensing
technologies), electromagnetic emitter/receiver tracking systems, and
exoskeletons.
Among the contact systems, the data glove, combined with visual feedback
for the position of a point in the hand, seems to yield good results. Data
gloves sense relative motion between adjacent movable links of the hand.
Commercial data gloves contain around 20 sensors and measure flexion
and abduction of the fingers, and palm-arching motion. Gloves developed
for research purposes (SIGMA Glove, University of Sheffield, Sensor Glove
from University of Berlin, etc.) may have up to 30 degrees of freedom.
Actual glove designs perform incomplete tracking, according to the
classification above. Sensor resolution can be good in theory, about 0.5
degrees and raw sensor data rate can be very fast, with typical values of
150 records/s.
11. 4
A couple of negative aspects of the data gloves are the following: if hand
positions need to be detected, the system needs to assume or measure a
priori the distances between finger joints. If that is not done, using the
same glove for individuals with different hand dimensions leads to a
significant increase in the error of the measurements. In addition, the
implicit model of the hand considers the joints as parallel and
perpendicular; in any case, no information can be extracted about the real
directions of the joints of the human hand. In many applications, a different
sensing device needs to be used in order to identify the motion of the wrist
and the location and orientation of the hand in space.
The list below contains a summary of commercial glove contact devices,
shown also in Figure 2.2.
• Optical (VPL Data glove) - Sensitive to hand size. Neoprene-fabric
glove with two fibre optic loops on each finger; each loop goes up to one
knuckle. That is the problem with the hand size. When hand is bent, light
escapes through small cuts of the fibre. Needs recalibration for each user.
DOF: 5 metacarpo-phalangeal joints, interphalangeal joint of the thumb, 4
proximal interphalangeal joints of the other fingers. Resolution: ¡ 1 degree
(in good conditions), rate: up to 160 Hz. Price $ 11000 approx. It seems
that the data glove is not being manufactured anymore.
• Resistor-based (Vertex Cyber glove). Versions of 18 or 22 sensors( 3
per finger 4 abduction sensors, palm arch sensor, and sensors for flexion
and abduction, resolution 0.5 degrees, data rate 90 records/s. Wireless
version. Price from $ 9800 approx.
12. 5
Figure 2.2: Types of controller [1]
Exoskeletons are rigid structures designed to follow, up to certain extent,
the motion of the hand.
They are in fact less used than gloves for applications in hand tracking. One
of the reasons is that it is difficult to create an exoskeleton that can adapt
to the hand deformation and still keep the accuracy in measuring certain
joint motions; they are mostly used to identify and track the motion of a
few degrees of freedom. Nakagawara et al. present a hand exoskeleton
used as a master to control a slave hand, see Figure 3. In this paper, some
of the common problems of exoskeletons are also identified. Kim et al.
present a wearable device called SCURRY which has characteristics of both
exoskeletons and data gloves.
13. 6
Figure 2.3: Position sensor [1]
Magnetic tracking follows the position and orientation of an emitter
attached to a point of the hand seems to yield a very good resolution and
it is a promising technology to be applied to hand tracking. One of the
disadvantages of electromagnetic tracking systems is interference: metallic
objects near the transmitter or receiver may affect the performance; this
may be a problem if other sensors need to be used to characterize a human
task, for instance force sensors. Magnetic trackers are also incomplete
tracking systems; for more rich motion identifications, more sensors need
to be placed in different points of the hand.
In general, contact systems are more intrusive, expensive, have cable
connections, are not portable (subject-dependent in many cases), and
require expertise to set up. Depending on the sensing system, they may be
affected by environmental noise. Some of the systems have very poor
accuracy, as specified in the list above, while others may have a better
accuracy than the non-contact (vision-based) systems.
2.2 Non-contact technologies
Non-contact technologies [1] refer almost exclusively to vision systems.
Vision-based systems can use a single camera, stereo cameras or
14. 7
orthogonally-placed cameras. Advantages of vision-based systems are that
they are less intrusive than any of the contact technologies; however, small
motions, certain views and occlusions may present problems in order to
identify the motion. This is partially overcome with the recovery of 3D
position and orientation information, but this in turn is difficult and
computationally expensive.
One of the main problems of the vision-based systems is that of the
automatic identification of the hand and the hand posture from the image.
In order to distinguish the interesting features in the hand, strategies such
as using colour, motion or edge information have been implemented.
Among other strategies, there are the following: skin-color detection (in
which the subject must wear long-sleeved clothes of a different colour),
colour cues, motion cues (when the hand is the only moving object), or
shape detection.
Much work has been done in this area, see for instance for an application
using a single camera. Typical systems nowadays consist of the
combination of the different techniques that have been proved to be useful.
Background suppression is a first step to pinpoint the subject. Then, particle
filters may be applied to identify different areas of the image of interest,
based on its shape and geometry (for instance, the tip of the fingers, the
wrist, etc.), or sometimes on the skin tone.
Those selected areas are used to build a 3-D or 2-D model of the image.
When using a single camera, occlusion becomes a bigger challenge. To
overcome this, some researchers use coloured gloves. Others locate the
hands in a non-occlusive view first and relay on tracking the motion from
the previously known location, assuming that the displacements between
frames is small; these systems aim to the global motion of the subject
(hand) by considering the motion of each pixel.
15. 8
In order to recognize hand gestures or hand configuration from the
interesting data, there are two basic approaches: appearance-based
approaches and model-based approaches.
Appearance-based, or shape detection, considers only a handful of gestures
that are quite different among them, fitting the actual gesture to the closest
in the database; see for instance Ong and Bowden. Similarly, Hoshino and
Tanimoto identify the hand posture by searching a similar image from a
vast database, optimized for quick searching.
Model-based approaches construct a 3-D model of the hand in order to
gather all the positional information. Model-based approaches are very
common in the literature, the main difference being the degree of
complexity and accuracy of the model used. Almost all models consist of
rigid links connected by joints. In the simplest cases, the joint is just a point
allowed to perform any rotation. A different model is studied in Bray et al.,
who use a stochastic meta-descent algorithm and a deformable hand model
to track hand motion.
A hybrid of the two approaches consists on considering a 3-D articulated
model that is compared to the image for a set different values of the joint
angles. The main inconvenient is that the searching process becomes very
long.
Only the model-based approach can yield the actual quantitative
information about the hand motion required for our research, and it is the
focus of the literature review below.
The 3-D identification and tracking with indirect transfer (using a model)
may rely on stick figures representing the skeleton, which gives the rigidity
and articulation to the body, 2-D contours, or 3-D volumes such as
cylinders, ellipses or blobs, which are fitted to the subject. The pose of the
model is predicted from the data and compared to the image. Holden and
Owens use a 21-dof hand model consisting of 15 dof for the hand plus a 6-
16. 9
dof wrist base that locates the hand arbitrarily in space. In order to sense
the motion they use a color-coded glove, where each link has a different
colour, and a single camera.
The 3-D models consist of 3-D shapes connected by joints with different
degrees of freedom. Hidden Markov models, coupled with the knowledge of
the geometry of the segments, are used in order to recognize and track
hand gestures from three-dimensional vision data. To identify the body
parts, they fit shapes to a controlled set of motions. Nolker and Ritter use
neural networks to locate the positions of the fingertips and to estimate the
three-dimensional pose of the hand, based on an articulated hand model
with 20 joint angles, some of which are considered coupled to simplify the
problem.
17. 10
CHAPTER 3: COMPONENTS OF ARTIFICIAL
ROBOTIC HAND
In constructing our hand, we made use of five servo motors. The servo
motor is at the base, which allows for the movement of the whole structure.
It facilitates the gripping of the hand.
Various components used in electro-mechanical structure are as follows:
1. Microcontroller: We have used Arduino Uno based on ATmega328.
2. Waste pipe: It is used to make the fingers of hand.
3. Electric wires: It is used for connection between the electronic
components.
4. Thread: It is used to provide the motion to waste pipes driven by servo
motors.
5. Servo motors: It acts as an actuator and provides motion to device.
6. Base: It provides the support to the whole structure.
7. Printed Circuit Board (PCB): It is used to make circuit board.
Simple structure of artificial robotic hand is shown below:
18. 11
3.1
3.1 Microcontroller
Figure 3.2: Arduino Uno [2]
3.1.1 Overview
The Uno is a microcontroller board based on the ATmega328P. It has 14
digital input/output pins (of which 6 can be used as PWM outputs), 6 analog
inputs, a 16 MHz quartz crystal, a USB connection, a power jack, an ICSP
header and a reset button. It contains everything needed to support the
Figure 2.1(a) Figure 2.1(b)Figure 3.1 (a): Rear view Figure3.1 (b): Inside view
Waste Pipe
(Finger)
Glove
(Hand)
Motor
(Actuator)
Thread
(Motion Control)
Figure 3.1: Robotic Hand
19. 12
microcontroller; simply connect it to a computer with a USB cable or power
it with an AC-to-DC adapter or battery to get started.
3.1.2 Technical Specifications
Microcontroller ATmega328P
Operating Voltage 5V
Input Voltage
(recommended)
7-12V
Input Voltage (limit) 6-20V
Digital I/O Pins
14 (of which 6 provide PWM
output)
PWM Digital I/O Pins 6
Analog Input Pins 6
DC Current per I/O Pin 20 mA
DC Current for 3.3V Pin 50 mA
Flash Memory
32 KB (ATmega328P)
of which 0.5 KB used by boot
loader
SRAM 2 KB (ATmega328P)
EEPROM 1 KB (ATmega328P)
Clock Speed 16 MHz
Length 68.6 mm
Width 53.4 mm
Weight 25 g
Table 3.1: Specifications [2]
20. 13
3.1.3 Power
The Uno board can be powered via the USB connection or with an
external power supply. The power source is selected automatically.
External (non-USB) power can come either from an AC-to-DC adapter
(wall-wart) or battery. The adapter can be connected by plugging a
2.1mm center-positive plug into the board's power jack. Leads from a
battery can be inserted in the GND and Vin pin headers of the POWER
connector.
3.1.4 Memory
The ATmega328 has 32 KB (with 0.5 KB occupied by the boot loader). It
also has 2 KB of SRAM and 1 KB of EEPROM.
3.1.5 Schematic and Design of Input and Output
Figure 3.3: Arduino design [3]
21. 14
3.1.6 Communication
The Uno has a number of facilities for communicating with a computer,
another Uno board, or other microcontrollers. The ATmega328 provides
UART TTL (5V) serial communication, which is available on digital pins 0
(RX) and 1 (TX). An ATmega16U2 on the board channels this serial
communication over USB and appears as a virtual com port to software on
the computer. The 16U2 firmware uses the standard USB COM drivers, and
no external driver is needed. However, on Windows, a .inf file is required.
The Arduino Software (IDE) includes a serial monitor which allows simple
textual data to be sent to and from the board. The RX and TX LEDs on the
board will flash when data is being transmitted via the USB-to-serial chip
and USB connection to the computer.
3.2 Servo Motor
Figure 3.4: Servomotor (Tower pro) [4]
22. 15
3.2.1 Overview
A servo motor is a dc, ac, or brushless dc motor combined with a position
sensing device (e.g. a digital decoder). In this section, our discussion will
be focused on the three-wire DC servo motors that are often used for
controlling surfaces on model airplanes. A three-wire DC servo motor
incorporates a DC motor, a gear train; limit stops beyond which the shaft
cannot turn a potentiometer for position feedback, and an integrated circuit
for position control. Of the three wires protruding from the motor casing,
one is for power, one is for ground, and one is a control input where a
pulse-width signals to what position the motor should servo. As long as the
coded signal exists on the input line, the servo will maintain the angular
position of the shaft. As the coded signal changes, the angular position of
the shaft changes.
Servos are extremely useful in robotics. The motors are small and are
extremely powerful for their size. A standard servo such as the Futaba S-
148 has 42 oz/inches of torque, which is pretty strong for its size. It also
draws power proportional to the mechanical load. A lightly loaded servo,
therefore, doesn't consume much energy. The guts of a servo motor are
shown in the picture below. You can see the control circuitry, the motor, a
set of gears, and the case. You can also see the 3 wires that connect to the
outside world. One is for power (+5volts), ground, and the white wire is
the control wire.
3.2.2 Servo working
So, how does a servo work? The servo motor has some control circuits and
a potentiometer (a variable resistor, aka pot) that is connected to the
output shaft. The potentiometer allows the control circuitry to monitor the
current angle of the servo motor. If the shaft is at the correct angle, then
the motor shuts off. If the circuit finds that the angle is not correct, it will
turn the motor the correct direction until the angle is correct. The output
23. 16
shaft of the servo is capable of travelling somewhere around 180 degrees.
Usually, it’s somewhere in the 210 degree range, but it varies by
manufacturer. A normal servo is used to control an angular motion of
between 0 and 180 degrees. A normal servo is mechanically not capable of
turning any farther due to a mechanical stop built on to the main output
gear. The amount of power applied to the motor is proportional to the
distance it needs to travel. So, if the shaft needs to turn a large distance,
the motor will run at full speed. If it needs to turn only a small amount, the
motor will run at a slower speed. This is called proportional control. How do
you communicate the angle at which the servo should turn? The control
wire is used to communicate the angle. The angle is determined by the
duration of a pulse that is applied to the control wire. This is called Pulse
Coded Modulation. The servo expects to see a pulse every 20 milliseconds
(.02 seconds). The length of the pulse will determine how far the motor
turns. A 1.5 millisecond pulse, for example, will make the motor turn to the
90 degree position (often called the neutral position). If the pulse is shorter
than 1.5 ms, then the motor will turn the shaft to closer to 0 degree. If the
pulse is longer than 1.5ms, the shaft turns closer to 180 degrees.
Figure 3.5: Servo Positions [4]
24. 17
As you can see in the picture, the duration of the pulse dictates the angle
of the output shaft (shown as the green circle with the arrow). Note that
the times here are illustrative and the actual timings depend on the motor
manufacturer. The principle, however, is the same.
3.3 Potentiometer
Figure 3.6: Potentiometer [5]
This is a very basic instrument used for comparing emf two cells and for
calibrating ammeter, voltmeter and watt-meter.
3.3.1 Working Principle of Potentiometer
The basic working principle of potentiometer [5] is very simple.
Suppose we have connected two batteries in head to head and tail to tale
through a galvanometer. That means the positive terminals of
both battery are connected together and negative terminals are also
connected together through a galvanometer as shown in the figure below.
Figure 3.7: Potential Difference across potentiometer [5]
25. 18
Here in the figure it is clear that if the voltage of both battery cells is exactly
equal, there will be no circulating current in the circuit and hence the
galvanometer shows null deflection. The working principle of
potentiometer depends upon this phenomenon.
Now let's think about another circuit, where a battery is connected across
a resistor via a switch and a rheostat as shown in the figure below, there
will be a voltage drop across the resistor. As there is a voltage drop across
the resistor, this portion of the circuit can be considered as a voltage
source for other external circuits. That means anything connected across
the resistor will get voltage. If the resistor has uniform cross section
throughout its length, the electrical resistance per unit length of
the resistor is also uniform throughout its length. Hence, voltage drop per
unit length of the resistor is also uniform. Suppose the current through
the resistor is i A and resistance per unit length of the resistor is r Ω. Then
the voltage appears per unit length across the resistor would be 'ir' and say
it is v volt.
Now, positive terminal of a standard cell is connected to point A on the
sliding resistor and negative terminal of the same is connected with a
galvanometer. Other end of the galvanometer is in contact with
the resistor via a sliding contact as shown in the figure above. By adjusting
this sliding end, a point like B is found where, there is no current through
the galvanometer, hence no deflection of galvanometer. That means emf
of the standard cell is just balanced by the voltage drop appears across AB.
Now if the distance between point A and B is L, then it can be written emf
of standard cell E = Lv volt. As v (voltage drop per unit length of the
sliding resistor) is known and L is measured from the scale attached to
the resistor, the value of E i.e. emf of standard cell can also be calculated
from the above simple equation very easily.
26. 19
Figure 3.8: Basic Working of potentiometer [5]
We said earlier in this section that one of the uses of potentiometer is to
compare emfs of different cells. Let's discuss how a dc potentiometer can
compare emfs of two different cells. Let's think of two cells whose emfs are
to be compared are joined as shown in the figure below. The positive
terminals of the cells and source battery are joined together. The negative
terminals of the cells are joined with the galvanometer in turn through a
two way switch. The other end of the galvanometer is connected to a sliding
contact on the resistor. Now by adjusting sliding contact on the resistor, it
is found that the null deflection of galvanometer comes for first cell at a
length of L on the scale and after positioning to way switch to second cell
and then by adjusting the sliding contact, it is found that the null deflection
of galvanometer comes for that cell at a length of L1 on the scale.
Let's think of the first cell as standard cell and its emf is E and second cell
is unknown cell whose emf is E1. Now as per above explanation,
E = Lv volt and
L1 = L1v volt
Dividing one equation by other, we get
27. 20
As the emf of the standard cell is known, hence emf of the unknown cell
can easily be determined.
Figure 3.9: Detailed Working of potentiometer [5]
28. 21
CHAPTER 4: CONTRUCTION AND WORKING
OF ARTIFICIAL HAND
In fabricating our robotic hand we have used five servo motor of capacity
9g, a waste pipe, thread for transferring motion from motor to the finger
in response to signals generated at microcontroller by potentiometer.
4.1 Testing of Artificial Robotic Hand
Project is been tested virtually on “Proteus” and real time testing is done
on bread board. For programming arduino is used due to ease of
programming as well as prebuilt functions for servo.
4.1.1 Virtual Testing
Steps involved:
1. Install Proteus and run proteus.exe file.
2. Select new project in ISIS mode.
3. Select the components required and design it according to the given
circuit below figure [4.1].
4. Upload the program file in microprocessor.
5. Now click on run and see the result by changing position of
potentiometer.
30. 23
4.1.2 Real Time Testing
Steps involved:
1. Place the components in the bread board as per the circuit design used
earlier.
2. Upload the program in microprocessor.
3. Now test the circuit, if not working well then check for any loose
connections and check again.
Figure 4.2: Real Testing Circuit (Breadboard)
4.2 Construction of Artificial Robotic Hand
Steps involved:
1. Cut 4 pieces of waste pipe of length 7 inches and for thumb 6 inches.
2. Make two holes of around 1 cm with the gap of 1.5 inches. Make single
hole for thumb after 2 inches.
Servo
Motor
Bread
Board
Potentiometer
USB
Cable
Arduino
UNO
31. 24
3. Now insert the tubes inside the fingers of glove.
4. Attach five pieces of thread of length 15 cm with the tip of each finger.
5. Connect the thread to servo motor clipper.
6. Now integrate the circuit on PCB.
7. Connect motor with integrated circuit.
8. Run the setup, if not working well check for any loose connections and
check again.
4.3 Working of Artificial Robotic Hand
4.3.1 Block Diagram
The block diagram of our work is as shown in Figure
4.3.2 Working
As the signal is generated by potentiometer the map () function converts
[0, 1023] value to the change in angle for servomotor [0, 180]. The signals
generated are in analog form which is needed to be converted in digital
signal by ADC port so that processor can work on it and send data to motor
via data port so that movement of fingers can be achieved.
READY AND
CALLING VIA
REQUEST
SIGNAL
SERVO WORK WITH
PERTICULAR
PROGRAMED OBJECT
FOR PICK
GRABBING OBJECT
BY CLAW
CODE FROM
ASSEMBLY
AREA TO
PWM GENARATE
BY ARDUINO
WORK DONE
TRANSMIT
SIGNAL
33. 26
CHAPTER 5: RESULT, CONCLUTION AND
FUTURE SCOPE
5.1 Result
The project gave very good result in virtual environment hence the
programming we are using verified to be correct, while working in real
environment we faced some challenges like distortion in signals due to
some loose connection or short circuit, Voltage supply, customizing own
PCB etc.
As the potentiometer turns it changes servo motor position, for change in
value by “5.6” in potentiometer gives a change of “1 degree” in servo motor
that is [0, 1023 :: 0,180] by this process motion of fingers is achieved.
5.2 Failure
At first the project was decided to be flex controlled, in which we are
supposed to use five flex sensor to control each finger motion but due to
lack of knowledge in the field of electronic project failed after giving few
successful test results, we lost the sensor and two out of five motors and
microprocessor so from there we analysed all the possible sources of error
at rectified it with our research over internet, time and consultancy with
faculty member and decide to shift to potentiometer as the working of flex
sensor is similar to that of potentiometer.
34. 27
5.3 Conclusion
The objectives of this project has been achieved which was developing the
hardware and software for a potentiometer controlled robotic arm. From
observation that has been made, it clearly shows that its movement is
precise, accurate, and is easy to control and user friendly to use. The
robotic arm has been developed successfully as the movement of the robot
can be controlled precisely. This robotic arm control method is expected to
overcome the problem such as placing or picking object that away from the
user, pick and place hazardous object in a very fast and easy manner.
5.4 Future Scope
The project is built on a wired model. It could further be developed to work
on wireless communication, thus allowing the user to move in an even
easier unrestricted manner. A clamper can be connected on the motor
which will allow the movements of the palm and allow picking and placing
of objects. Currently the potentiometer signal is being processed via a
digital computer; this could be eliminated by using a fast microprocessor
such as ARMv7, etc. It could also be possible to eliminate the ATmega32
altogether when ARMv7 is being used. The microprocessor could take the
input from the neuro devices and smoothen it and then generate the
corresponding PWM signal itself to actuate the servo motors.
