By Dirga Pratama
11110026
A thesis submitted to the Faculty of
ENGINEERING AND INFORMATION TECHNOLOGY
in partial fulfillment of the requirements for the
BACHELOR’S DEGREE in
MECHATRONICS ENGINEERING
SWISS GERMAN UNIVERSITY EduTown BSD City
Tangerang 15339 Indonesia
Revision after Thesis Defense on July 16th 2014
Dirga Pratama STATEMENT BY THE AUTHOR
I hereby declare that this submission is my own work and to the best of my knowledge, it contains no material previously published or written by another person, nor material which to a substantial extent has been accepted for the award of any other degree or diploma at any educational institution, except where due acknowledgement is made in the thesis.
Dirga Pratama
_____________________________________________
Student Date
Approved by:
Dr. Ir. Prianggada Indra Tanaya, MME
_____________________________________________
Thesis Advisor Date
Dr. Ir. Gembong Baskoro, M.Sc
_____________________________________________
Dean Date
Dirga Pratama ABSTRACT
DESIGNING AND DEVELOPING A NEURAL NETWORK PATH PLANNER SYSTEM ON MEKANUM WHEEL MOBILE ROBOT PLATFORM
By Dirga Pratama
Dr. Ir. Prianggada Indra Tanaya, MME, Advisor
SWISS GERMAN UNIVERISTY
The objective of this thesis work is to develop a mekanum wheel mobile robot platform that has a neural network approach in generating the shortest path while avoiding collisions.
A mekanum wheel mobile robot platform was designed during Mechatronics System Design 2 project in 7th semester. A Planning, Execution, and Monitoring (PEM) architecture is applied to the system hierarchy to divide the processes into several smaller modules. The monitoring module keeps track of the robot movement. The planner module generates a trajectory to reach the destination point using Artificial Neural Network (ANN) architecture. The proposed ANN is a three layer “100-10- 100” network which consists of an input, a hidden, and an output layer. This ANN model is trained through a supervised learning method using back-propagation algorithm. The network is continuously trained throughout the thesis work. The path is converted into a set of command sequences in data packets which then will be sent to the execution module to be executed. The framework is developed using Qt Creator under C++ programming language and Arduino IDE.
Keywords: Artificial Intelligence, Artificial Neural Network, Execution, Mekanum wheel, Mobile robot, Monitoring, Path planning
Dirga Pratama
© Copyright 2014 by Dirga Pratama All rights reserved
Dirga Pratama DEDICATION
I dedicate this thesis to myself.
Dirga Pratama ACKNOWLEDGEMENTS
I give my greatest thanks to God for His endless blessings throughout this thesis.
I would like to express my sincere gratitude to my thesis advisor, Dr. Ir. Prianggada Indra Tanaya, MME for his continuous support and guidance throughout the thesis.
Thank you for inspiring me to be a better student each day.
I would like to thank my parents, Rahardi and Susan Darmawan for all their support emotionally and financially during these four years.
Special thanks to Addythia Saphala and Saras Safitri for helping me with some difficulties during the thesis work.
I would also like to thank all students of Mechatronics batch 2010 with whom I shared all 8 semesters in SGU. Thank you for all your support and encouragement.
May all the best things happen to our lives.
Dirga Pratama TABLE OF CONTENTS
STATEMENT BY THE AUTHOR ... 2
ABSTRACT ... 3
DEDICATION ... 5
ACKNOWLEDGEMENTS ... 6
TABLE OF CONTENTS ... 7
LIST OF FIGURES ... 11
LIST OF TABLES ... 14
CHAPTER 1 - INTRODUCTION ... 15
1.1 Background ... 15
1.2 Thesis Purpose ... 16
1.3 Thesis Problem ... 16
1.4 Thesis Scope ... 16
1.5 Thesis Limitation ... 16
1.6 Thesis Organization ... 17
CHAPTER 2 - LITERATURE REVIEW ... 18
2.1 Introduction ... 18
2.2 Theoretical Review ... 18
2.2.1 Mekanum Wheel ... 18
2.2.2 Artificial Neural Network ... 20
2.3 Review of Existing Researches and Projects ... 22
2.3.1 Software Design and Analysis of Trajectory Path Planning in Mecanum Wheel Omni-Directional Robot [4] ... 22
Dirga Pratama
Mecanum Wheel Robot [5] ... 25
2.3.3 Development of Execution and Monitoring Architecture Modules for an Autonomous Human Follower Transporter Robot [6] ... 26
2.3.4 Neural Network Model for Path-Planning Of Robotic Rover Systems [7] . 28 2.3.5 Neural Network Based Path Planning and Navigation of Mobile Robots [8] ... 30
2.4 Concluding Remarks ... 33
CHAPTER 3 – METHODOLOGY ... 34
3.1 Introduction ... 34
3.2 System Design and Overview ... 34
3.3 Process Block Diagram ... 35
3.4 Mechanical Design ... 36
3.4.1 Base Plate and Body Design ... 37
3.4.2 Mekanum Wheel ... 38
3.4.3 Kinematic Analysis of Mekanum Wheel Robot ... 39
a. Inverse Kinematics ... 39
b. Forward Kinematics ... 42
3.4.4 Torque Calculation ... 43
3.5 Electrical Design ... 45
3.5.1 Faulhaber 12V Brushless DC Motor ... 45
3.5.2 Incremental Rotary Encoder ... 46
3.5.3 L298 H-Bridge Motor Driver ... 47
3.5.4 Arduino Mega 2560 Microcontroller Board ... 48
3.5.5 ODROID-U3 Mini PC ... 49
3.5.6 GS 12V Battery GTZ5S ... 50
Dirga Pratama
3.6.1 8 Basic Movement Test ... 51
3.6.2 Odometry ... 53
3.6.3 Neural Network ... 56
a. Training ... 57
b. Testing ... 63
3.6.4 Path to Command Converter ... 63
3.6.5 Qt Serial Port ... 66
3.7 Development Tools ... 68
3.7.1 Xubuntu 13.10 32-bit (Linux)... 68
3.7.2 Qt SDK 5.1.1 ... 69
3.7.3 Arduino IDE ... 69
3.7.4 VNC Viewer Remote Desktop Client... 69
3.8 Technical Specification ... 70
CHAPTER 4 – RESULTS AND DISCUSSIONS ... 71
4.1 Introduction ... 71
4.2 8 Basic Movement Accuracy Test Result ... 71
4.3 Weight Training Result ... 75
4.4 Path Generator Test Case ... 78
4.5 Path Executor Test Case ... 81
CHAPTER 5 – CONCLUSIONS AND RECOMMENDATIONS ... 83
5.1 Conclusions ... 83
5.2 Recommendations and Future Developments ... 84
GLOSSARY ... 85
REFERENCES ... 86
Dirga Pratama
APPENDIX A – Robot mechanical design... 89
APPENDIX B – Electrical diagram ... 92
APPENDIX C – Electrical component datasheet ... 93
C.1 Faulhaber 12V Brushless DC Motor (2342012 CR) ... 93
C.2 L298 H-Bridge Motor Driver ... 94
C.3 Arduino Mega 2560 Microcontroller Board ... 96
C.4 ATMega2560 ... 102
C.5 7805 Voltage Regulator ... 104
C.6 ODROID-U3 ... 105
APPENDIX D – Programming code ... 106
APPENDIX E – Bill of materials ... 128
CURRICULUM VITAE ... 129
Dirga Pratama LIST OF FIGURES
Figure 2.1 Mekanum wheel………... ..17
Figure 2.2 Two types of mekanum wheel arrangement [2]………... ..17
Figure 2.3 Vector movement of first arrangement [2]………... ..18
Figure 2.4 Vector movement of second arrangement [2]……….. ..18
Figure 2.5 Biological neural network [3]………... ..19
Figure 2.6 Architecture of a typical ANN [3]……… ..19
Figure 2.7 Roller radius plotted versus roller length, using Mathplotlib [4]………. ..21
Figure 2.8 Mekanum roller calculation [4]……… ..22
Figure 2.9 (a) Mekanum wheel framework before bending, (b) Finished product, (c)&(d) Rollers [4]………. ..22
Figure 2.10 Trajectory path planning design flow chart [4]……….. ..23
Figure 2.11 Master and Slave configuration [5]……… ..24
Figure 2.12 Human Follower Transporter Robot [6]………. ..25
Figure 2.13 Human Follower Transporter Robot process block diagram [6] ……... ..26
Figure 2.14 Proposed 2-3-4 ANN model [7]………. ..27
Figure 2.15 Training engine for the proposed ANN [7]……… ..28
Figure 2.16 Rover path-planning simulation [7]………..28
Figure 2.17 Obstacle description neural network [8]………. ..30
Figure 2.18 The annealing network of rectangular obstacle [8]……… ..30
Figure 2.19 Simulation example [8]……….. ..30
Figure 2.20 Algorithm block diagram [8]……….. ..31
Figure 3.1 System hierarchical structure………... ..33
Figure 3.2 PEM architecture model………... ..34
Figure 3.3 Process block diagram……….. ..35
Figure 3.4 Frame design……… ..36
Figure 3.5 Combined base plate and design……….. ..36
Figure 3.6 Mekanum wheel and its hub (left), after assembly (right)………... ..37
Figure 3.7 Mekanum wheel configuration………. ..37
Figure 3.8 Top-view vehicle coordinate system……… ..38
Figure 3.9 Vector diagram of mekanum roller……….. ..40
Dirga Pratama
Figure 3.11 Overview of electrical system... ..44
Figure 3.12 Faulhaber 12V Brushless DC Motor……….. ..45
Figure 3.13 Output encoder signal for each phase………. ..46
Figure 3.14 L298 H-Bridge motor driver……….. ..46
Figure 3.15 H-Bridge circuit……….. ..47
Figure 3.16 Arduino Mega 2560 microcontroller board………..48
Figure 3.17 ODROID-U3 mini PC……… ..48
Figure 3.18 GS 12V battery GTZ5S……….. ..50
Figure 3.19 Mekanum wheel right movement………... ..51
Figure 3.20 Right movement function………... ..51
Figure 3.21 Odometry flow chart………...54
Figure 3.22 Proposed ANN model………...55
Figure 3.23 Three-layer back-propagation neural network [3]……….. ..57
Figure 3.24 Example of training input and its desired output………..57
Figure 3.25 Activation functions of a neuron [3]……….. ..58
Figure 3.26 Learning process flow chart………... ..61
Figure 3.27 Output layer result……….. ..62
Figure 3.28 Path selection function and its output………. ..63
Figure 3.29 Conversion path to command function………...64
Figure 3.30 Qt odometry user interface………. ..66
Figure 3.31 Data array………... ..66
Figure 4.1 Forward movement, 30cm and 50cm………... ..71
Figure 4.2 Forward movement, 100cm……….. ..72
Figure 4.3 Right movement, 30cm, 50cm, and 100cm……….. ..72
Figure 4.4 Upright movement, 30cm, 50cm, and 100cm……….. ..73
Figure 4.5 Clockwise movement………... ..73
Figure 4.6 Line chart of movement deviations……….. ..75
Figure 4.7 Number of iterations plotted versus learning rate……….... ..76
Figure 4.8 Training process result………. ..76
Figure 4.9 Sum of squared error plotted against number of iterations……….. ..77
Figure 4.10 Training map test with its desired output………... ..78
Dirga Pratama
Figure 4.12 Map test 1………... ..79
Figure 4.13 Map test 1 result………. ..79
Figure 4.14 Map test 2………... ..80
Figure 4.15 Map test 2 result………. ..80
Figure 4.16 Path arrays of map test 1 and map test 2……… ..81
Figure 4.17 Command list for map test 1 and map test 2……….. ..81
Figure 4.18 Complete output program for map test 1……… ..82
Dirga Pratama LIST OF TABLES
Table 2.1 Analogy between biological and artificial neural networks……….. ..20
Table 3.1 Encoder specifications………... ..45
Table 3.2 ODROID-U3 mini PC specifications………...49
Table 3.3 8 Basic movement and wheel rotation………... ..52
Table 3.4 List of robot commands and its direction……….. ..63
Table 3.5 Movement command description………... ..67
Table 4.1 Forward movement error………... ..74
Table 4.2 Right movement error……… ..74
Table 4.3 Upright movement error……… ..74
