28
VISION BASED AUTOMATED FORMATION FOR MULTI ROBOT COOPERATION MOHD MUHAIMIN BIN MAHMOOD UNIVERSITI TEKNOLOGI MALAYSIA
VISION BASED AUTOMATED FORMATION FOR
MULTI ROBOT COOPERATION
MOHD MUHAIMIN BIN MAHMOOD
UNIVERSITI TEKNOLOGI MALAYSIA
VISION BASED AUTOMATED FORMATION FOR
MULTI ROBOT COOPERATION
MOHD MUHAIMIN BIN MAHMOOD
A thesis submitted in fulfilment of the
requirement for the award of the degree of
Master of Engineering (Electrical)
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
AUGUST 2016
iii
Specially dedicated to:
My beloved family, lecturers and all friends for their external support,
encouragement, and inspiration throughout my journey of education.
MAY ALLAH BLESS US
iv
ACKNOWLEDGEMENT
First of all, I would like to thank Allah SWT for giving me strength to complete
this project successfully on time. I would like to express my gratitude to my project
supervisor, Prof Dr Shamsudin H. M. Amin who willing to accept me as his student to
carry out project under him. Besides, I would like to thank Prof Dr Shamsudin for the
passionate guidance and advises he gave upon my throughout entire project. It would be
difficult to complete this project without his support and understanding.
However, I wish to deeply indebted to my family member for giving me spirit
along with the support assisting my project and throughout years in UTM. Their
blessings were the main effort for me to overcome all the hardships and obstacles that I
faced. Not forgotten to their hardworking to provide me financial support to ensure my
successfulness in this project.
Next, my sincerest appreciation goes to my friends and especially to my
laboratory mates who always giving me valuable guidance, suggestions, kindness, and
valuable time during the accomplishment of this project. Lastly, I would like to extend
my deep appreciation for the laboratory technicians for giving me as mush patient and
guidance and support to enhance my project. Thank you so much.
v
ABSTRACT
In a multi robot system, robots are required to cooperate with each other, and
therefore should have the ability to make their own decision based on multiple input
sensors not only from the robots, but also from nearby robots. The task of carrying
oversized objects of different shapes poses a challenge in selecting an appropriate group
formation. Hence, the main objective of this project is to establish an algorithm that
enables multi robot system to carry large load by automatically selecting the required
group formation to successfully execute the task. At first, a robot will need to identify
the shape of the object (oversized bar, rectangular, square or circular shapes). Then, the
robots will form a suitable formation to carry the object. There are two main problems in
this project. First, the capability of the robot to identify the shape of the object because
the object’s image will be a bit skew form the actual shape, due to the slanting angle of
the camera used to detect the shapes of the objects. The second challenge is maintaining
the formation of the robots, while carrying the load on top of the robots, to a specified
destination. A multi robot system, built in-house is used in the experiments to investigate
the performance of the algorithm proposed. Algorithms implemented in this project are
leader-follower and behaviour based strategy. One of the robots will operate as the
command giver or the leader to the other robots. The algorithm consists of
communication strategies and autonomous decision making capability. The robot will be
communicating with each other using Xbee wireless modules and extracting the
behaviour of the other robots. Sensors placed around the body of the robots are utilized
to detect their relative distance, and hence, used to maintain their formation, so as to
prevent the load from falling down. All the decisions are made by the robots
autonomously via the onboard controllers. The multi robot system is shown to be able to
autonomously determine the shape of the different oversized objects, thus appropriately
change into formations capable of transporting large objects to a specified destination
point autonomously, with no outside intervention.
vi
ABSTRAK
Dalam sistem berbilang robot, robot-robot perlu untuk saling bekerjasama,
dilengkapi dengan kemampuan membuat keputusan sendiri bergantung kepada peranti-
peranti input yang bukan sahaja daripada robot tersebut, bahkan daripada robot
berdekatan. Tugasan untuk membawa objek besar yang terdiri daripada beberapa bentuk
yang berlainan adalah mencabar dalam menentukan susunan robot yang sesuai. Objektif
utama projek ini adalah melaksanakan satu algoritma yang membolehkan berbilang
robot untuk mengangkut objek besar secara automatik dan menentukan susunan
kumpulan robot berdasarkan bentuk objek yang perlu diangkut. Pada peringkat awal,
sebuah robot perlu mengenal pasti bentuk objek yang akan diangkut (bentuk batang
panjang, segi empat tepat, segi empat sama atau bulatan). Seterusnya, robot-robot
tersebut akan membuat susunan yang sesuai dengan bentuk dan saiz objek, untuk
mengangkut objek tadi. Terdapat dua masalah utama dalam projek ini. Pertama,
kemampuan robot untuk mengenal pasti bentuk objek kerana imej objek adalah sedikit
senget daripada bentuk sebenar, berikutan sudut kamera tidak terus dari atas objek.
Cabaran kedua adalah mengekalkan susunan kumpulan robot tadi apabila objek diangkut
di atas robot, ke destinasi yang ditetapkan. Sistem berbilang robot yang dibina sendiri
digunakan dalam eksperimen ini bagi mengenal pasti kemampuan algoritma yang
dicadangkan. Algoritma yang digunakan dalam eksperimen ini adalah ketua-pengikut
dan strategi berdasarkan kelakuan. Satu robot akan berfungsi sebagai pemberi arahan
kepada robot lain. Algoritma ini ada strategi komunikasi dan kemampuan membuat
keputusan secara automatik. Robot-robot ini akan berhubung dengan menggunakan
modul tanpa wayar Xbee dan ekstrak kelakuan robot satu lagi. Peranti yang diletakkan
sekeliling badan robot diguna untuk mengetahui jarak relatif yang digunakan untuk
mengekalkan susunan robot bagi mengelakkan beban daripada jatuh. Semua keputusan
dibuat robot itu sendiri secara automatik dengan kawalan litar papan. Sistem berbilang
robot menunjukkan ia mampu secara automatik untuk menentukan bentuk objek besar
dan menentukan susunan yang mampu membawa objek tersebut daripada titik destinasi
yang ditentukan tanpa bantuan daripada luar.
vii
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF FIGURES xi
LIST OF TABLE xv
LIST OF APPENDICES xvi
1 INTRODUCTION 1
1.1 Overview 2
1.2 Problem Statement 4
1.3 Research Questions 4
1.4 Research Objectives 5
1.5 Scope of Research 5
1.6 Thesis Outline 6
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2 LITERATURE REVIEW 7
2.1 Introduction 8
2.2 Centralized Group Control 9
2.2.1 Leader-follower type cooperative
control
10
2.3 Environment Adaptive 12
2.3.1 Modular Robot 13
2.4 Image Comparing Algorithm 14
2.4.1 Sift Method 15
2.5 UTM Multi Agent 17
2.6 Summary 19
3 METHODOLOGY 21
3.1 Robot Software Programming 21
3.2 Robot Architecture 22
3.3 Robot Component 24
3.3.1 A Laptop 24
3.3.2 Webcam 25
3.3.3 Ultrasonic Range Rinder (MAXsonar
EZI)
25
3.3.4 Analog Distance Sensor (GP2YOA21) 26
3.3.5 Incremental Rotary Encoder 27
3.3.6 USB Hub and USB Hub Power 27
3.3.7 Vexta brushless motor with wheel and
motor driver
28
3.3.8 Battery Charger 29
3.3.9 SKXbee 29
3.3.10 Interface Free Controller, IFC 30
ix
3.3.11 Switches and LED 31
3.4 Automated Formation Selection using Vision
Based on Scale Invariant Formation Transform
(SIFT)
32
3.5 Robot Formation 36
3.5.1 Square Object 36
3.5.2 Triangular Object 38
3.5.3 Rectangular Bar 39
3.5.4 Circular Object 41
3.6 Summary 43
4 RESULTS AND DISCUSSION 45
4.1 Controller 45
4.2 Obstacle Anti-Colliding Behavior 46
4.3 Reaching Destination 47
4.4 Experiment 47
4.4.1 Experiment 1 48
4.4.2 Experiment 2 50
4.4.3 Experiment 3 53
4.4.4 Experiment 4 55
4.5 Object Shape Recognition 57
4.6 Robot Tracing 62
4.6.1 Square Shape Object 62
4.6.2 Triangular Shape Object 63
4.6.3 Rectangular Bar Shape Object 64
4.6.4 Circular Shape Object 65
4.7 Result Comparison with Theoretical Expectation 66
4.7.1 Square Object 66
x
4.7.2 Triangular Object 67
4.7.3 Rectangular Bar Object 68
4.7.4 Circular Object 69
4.8 Discussion 70
5 CONCLUSION 73
5.1 Conclusion 73
5.2 Achievement and Contribution 74
5.3 Future Work 74
REFERENCES 76
Appendices A-C 82
xi
LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 Environment-adaptation using tilt sensors and 3D topology of
module with multiple tilt constraints
13
2.2 Top: A skeleton view of the modular robot. A: Pivot module; B:
Supporting group. A and B are jointly viewed as an agent.
Surface group C is viewed as an inter-agent link. Bottom: The
desired shape (level surface) is specified with a set of local
desired tilt angles (constraints).
14
2.3 Extracting all of the SIFT features that it can find in both
images, match point
17
2.4 UTM Multi Agent Robot 19
3.1 UTM Multi Agent Robot System Wiring 22
3.2 UTM Multi Agent Robot Front and Rear View 23
3.3 UTM Multi Agent Robot Laptop 24
3.4 UTM Multi Agent Robot Webcam 25
3.5 UTM Multi Agent Robot ultrasonic Range Finder 25
3.6 UTM Multi Agent Robot Analog Distance Sensor 26
3.7 UTM Multi Agent Robot Rotary Encoder 27
3.8 UTM Multi Agent Robot USB Hub 27
3.9 UTM Multi Agent Robot Brushless Motor 28
3.10 UTM Multi Agent Robot Battery Charger 29
3.11 UTM Multi Agent Robot SKXBee 29
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3.12 UTM Multi Agent Robot IFC 30
3.13 UTM Multi Agent Robot Top View 32
3.14 Environment-adaptation using tilt sensors and 3D topology of
module with multiple tilt constraints
33
3.15 Flow leader and follower robots 34
3.16 Limit switch position on top of multi robot 35
3.17 Sample of Square Object 36
3.18 Formation of the robot if the object is square shape 37
3.19 Sample of Triangular Object 38
3.20 Formation of the robots if the object is triangle shape 39
3.21 Sample of Rectangular Bar Object 40
3.22 Formation of the robots if the object is rectangular bar shape 40
3.23 Sample of Circular Object 41
3.24 Formation of the robots if the object is circular shape 42
4.1 Multi Robot Controller 46
4.2 Obstacle Avoidance Flowchart 46
4.3 Initial multi robot position 48
4.4 Leader robot inspect square object 48
4.5 Multi robot make suitable position 49
4.6 Square object is putted on the robots 49
4.7 Robots move forward 50
4.8 Robots safely arrived at finishing line 50
4.9 (a) Robot positioned in straight line
(b) Leader Robot recognize shape of the object
(c) Follower robot move beside leader robot
51
4.10 Triangular Object placed on robots 51
4.11 Robots move forward and maintain the formation 52
4.12 Robots successfully arrived at the destination 52
4.13 (a) Robot positioned in straight line
(b) Leader Robot recognize shape of the object
53
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(c) Follower robot move beside leader robot
4.14 Rectangular Bar object placed on robots 54
4.15 Robot moved slowly to ensure the object not fall down 54
4.16 Robots successfully arrived at the destination 55
4.17 (a) Robot positioned in straight line
(b) Leader Robot recognize shape of the object
(c) Follower robot moved and make formation suitable for
circular object
56
4.18 Circular shape object putted on robots 56
4.19 (a) Robots moved toward destination
(b) Robots successfully arrived at the destination
57
4.20 (a) Image of square in computer database
(b) Image of square shape object capture by robot webcam
58
4.21 Sentences print on the laptop monitor when analyzing square
shape object
58
4.22 (a) Image of triangular in computer database
(b) Image of triangular shape object capture by robot webcam
59
4.23 Sentences print on the laptop monitor when analyzing triangular
shape object
59
4.24 (a) Image of rectangular in computer database
(b) Image of rectangular bar shape object capture by robot
webcam
60
4.25 Sentences print on the laptop monitor when analyzing
rectangular shape object
60
4.26 (a) Image of circular in computer database
(b) Image of circular shape object capture by robot webcam
61
4.27 Sentences print on the laptop monitor when analyzing circular
shape object
61
4.28 Tracing of UTM Multi Robot, when it detect square shape
object until it reach finishing line safely
62
4.29 Tracing of UTM Multi Robot, when it detect triangular shape 63
xiv
object until it reach finishing line safely
4.30 Tracing of UTM Multi Robot, when it detect rectangular bar
shape object until it reach finishing line safely
64
4.31 Tracing of UTM Multi Robot, when it detect circular shape
object until it reach finishing line safely
66
4.32 (a) Theoretical Square Shape object
(b) Image of square shape object capture by robot webcam
66
4.33 (a) Theoretical Square Shape Formation
(b) Actual UTM Multi Robot Formation for square shape object
67
4.34 (a) Theoretical Triangular Shape object
(b) Image of triangular shape object capture by robot webcam
67
4.35 (a) Theoretical Triangular Shape Formation
(b) Actual UTM Multi Robot Formation for triangular shape
object
68
4.36 (a) Theoretical Rectangular Bar Shape object
(b) Image of rectangular bar shape object capture by robot
webcam
68
4.37 (a) Theoretical Rectangular Bar Shape Formation
(b) Actual UTM Multi Robot Formation for rectangular bar
shape object
69
4.38 (a) Theoretical Circular Shape object
(b) Image of circular shape object capture by robot webcam
69
4.39 (a) Theoretical Square Shape Formation
(b) Actual UTM Multi Robot Formation for square shape object
70
xv
LIST OF TABLE
TABLE NO. TITLE PAGE
2.1 UTM Multi Agent Robot Hardware 19
xvi
LIST OF APPENDICES
APPENDIX TITLE PAGE
A UTM Multi Agent Robot Hardware 82
B UTM Multi Agent Robot Software – Leader 116
C UTM Multi Agent Robot Software – Follower 127
CHAPTER 1
INTRODUCTION
Distributed robotic system became an interesting field of study, which bloom
around recent millennium era because it is more convenient against single robot system
[1]. It can be classified to many areas such as communication, dealing with difficult task
[2], task allocation and control [3], localization [4], exploration, large object
transportation [5], motion coordination and biological inspiration. All these topics are
discussed in multi robot system.
Object transport and manipulation
Robots will be collaborated to carry, move or manipulate object. Most of the projects
only use simulation or software verification. The projects on object transportation
normally study on constrain motion, numbers of robot, mechanism to hold and grasp
load, undefined workspace and so on. There are also various methods in handling the
object [6]. It can be pushed, griped, and pull using rope and others.
Motion coordination
In this application, it involves path planning [7,8], traffic control, formation generation
and maintaining the formation. The issues that commonly come out are about target
2
tracking, target search and multi-robot docking behavior. The approach used normally
marking some checkpoints for the robot, using dynamic priority assignment, generation
of cooperative gait and vision based robot. [9]
Formation control is the popular subject in multi robot area of study where the robot
communicates and able to move to destination point in desired robot orientation. The
formation control could be a bit complex in term of stability and convergence when the
multi robot is tasked to transport an object to a target point.
Reconfiguration Robotics
In this research area, reconfiguration robotic is about multi robot that can be adjusted or
adjusting itself either manual or automatic depend on the current situation [10,11]. These
types of robot give a great versatility and robustness, because it can generate desire
shape to fulfill certain function which changes from time to time.
1.1 Overview
Since the past decades, many robots have been built to fulfill human needs and
interests. In movies also, the development of robotics have grown, for example the
movie ‘Transformer’. The movie has been made in many series, which indicates that
many people around the world like robots and machinery thing. Competitions involving
robots nowadays are numerous to improve the design of robotic system and to generate
new ideas in the robotics field such as ‘Robot Soccer’, ‘ABU Robocon’, ‘Robo Sumo’,
‘Robofest’ and many more.
There are many objectives on robotic research because of the usage of robot is
very wide. These include the usage in dangerous environment, complete difficult tasks
[4] given, assist human in daily work or routine, transport large objects [5], and doing
3
repeated task over and over again. In some cases use of a single robot is not sufficient
and can only cover very limited tasks. So, it requires more than one robot to work in
collaboration, so that the task will be completed successfully. In the real world
environment, the robots must alter their formation by simple navigation strategies [1],
behavior based control [10,11], virtual structures [7,8], formation vector [3], hierarchical
formation, omnidirectional vision [9], etc.
There is an increasing research interest in systems composed of multiple
autonomous mobile robots exhibiting cooperative behaviors. Groups of mobile robots
are constructed, with the aim of studying such issues as group architecture, resource
conflict, origin of cooperation, learning, and geometric problems. Below are some of
the advantages of using multi robot systems against single robot systems [2]:
1) A large range of task domains
2) Greater efficiency [12]
3) Improved system performance
4) Fault tolerance
5) Robustness [12]
6) Lower economic cost [13]
7) Ease of development
8) Distributed sensing and action
As yet, few applications of cooperative robotics have been reported, these
include cooperative manipulation [10], navigation, and planning that is used for a group
of airplane maneuvers [7]. Supporting theory is still in its formative stages [14].
4
1.2 Problem Statement
Navigation strategy, maneuvering, manipulation, and mission planning are main
problem in mobile research study [15]. The implementation of behavior-based system
rather than common mobility systems for a mobile robot field gives a challenging in part
of research aspect to this multi robot study. Existing UTM researches only deal with
robot navigation and load transportation. Designing controller for behavior based multi
robot system is challenging because of two factors [16]. Firstly, a behavior-based system
has possibility to act on its own because the surrounding changes very fast and the robot
has many inputs to be processed. Next, its algorithm model is not complete yet and
mostly still unknown. So, in order to design a controller for this dynamic system, it
needs to use some approaches depending on knowledge, or model. In the case of a robot,
this system consists of the robot itself and the environment in which it operates for
example in a laboratory. However, the situation is different when a robot is worked in
the real world environment with many things need to be considered and take into
account.
1.3 Research Questions
In this project, researcher tries to find out how the multi robots cooperate
together to complete a given task that changes depending on the environment or
stimulate condition? Will the robots able to differentiate the dissimilarity of the situation
and from that able to make good respond together, not just a robot?
5
1.4 Research Objective
Multi agent robot system consists of mobile robots with similar attribute and can
move freely. This team can be operated by minimum presence of two-robot. The
outcomes of this collaboration of robots are:
1) to bring an oversized object to a fixed defined destination using a leader-follower
strategy automatically.
2) to identify what formation that the robots will make, depending on the image of
the object that it captured without external support.
3) to design a behavior based controller (software) for a team of multi agent robots
to do a single task regarding communication and cooperation between both robot.
1.5 Scope of Research
In the limitation of the time of the project, author will state some of scopes of the
project in order for the project work perfectly and complete in the given time frame.
Firstly, in this project, UTM Multi Robot that is already available in Robotic Laboratory
is used. This is because it needs a vast amount of time and effort to come out with a
brand new robot.
Second, the technologies that have been used depend on what already build-in
with the UTM Multi Robot. The robot equipped with many sensors and system, but the
technologies are a bit outdated.
6
Next, the experiments to try out the early hypothesis have been carried out in the
Fakulti Kejuruteraan Elektrik, UTM Robotic Laboratory because the robot is heavy and
it easier to commence the experiment or doing modification on the spot.
Lastly, this project will use Visual Studio as programing software. Visual studio
enable programmer to interface the sensor with graphic on the screen. This attribute
indicates either the sensors working or not, by graphic interface on laptop.
1.6 Thesis Outline
This thesis consists of five chapters in total. In the second chapter, it will
describe the study of previous researches. It will discuss about leader follower technique
in multi robot system, environment adaptive method and image processing in
recognizing object that will be transported. The details about the robot used in this
project shall also be described in this chapter.
Next, Chapter 3 explains about the methodology of this project from hardware
part to software thing. It includes how the robot recognized the shape of the object in
front; gave command to the follower robot, formed suitable formation, and transported
load to the destination successfully.
Then results from the experiment are presented in Chapter 4. It also contains
discussion about the results, including the comparison of the result obtained with the
expected outcome (theory). The result will also be presented not just by the camera
snapshot in the experiment that been carried out. It also will be presented in trace form,
in order to clearly present what really happen in the experiment.
Last chapter, will conclude the project that has been done and proposed
suggestion for future project in multi robot system field of study.
76
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