Reconfigurable Validation Model for Identifying Kinematic Singularities and Reach Conditions for

212 Pages·2017·12.98 MB·English

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Electronic Theses and Dissertations Theses, Dissertations, and Major Papers
2014
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Luv Aggarwal
University of Windsor
Follow this and additional works at: https://scholar.uwindsor.ca/etd
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Aggarwal, Luv, "Reconfigurable Validation Model for Identifying Kinematic Singularities and Reach
Conditions for Articulated Robots and Machine Tools" (2014). Electronic Theses and Dissertations. 5219.
https://scholar.uwindsor.ca/etd/5219
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Reconfigurable Validation Model for Identifying Kinematic
Singularities and Reach Conditions for Articulated Robots and
Machine Tools
By
Luv Aggarwal

A Thesis

Submitted to the Faculty of Graduate Studies
through the Department of Mechanical, Automotive and Materials Engineering
in Partial Fulfillment of the Requirements for
the Degree of Master of Applied Science
at the University of Windsor

Windsor, Ontario, Canada
2014

© 2014 Luv Aggarwal
Reconfigurable Validation Model for Identifying Kinematic Singularities and Reach
Conditions for Articulated Robots and Machine Tools
By
Luv Aggarwal

APPROVED BY:

______________________________________________
Dr. Z. Pasek
Industrial and Manufacturing Systems Engineering

______________________________________________
Dr. J. Johrendt
Mechanical, Automotive and Materials Engineering

______________________________________________
Dr. R. J. Urbanic, Advisor
Mechanical, Automotive and Materials Engineering

June 23, 2014
DECLARATION OF CO-AUTHORSHIP / PREVIOUS PUBLICATION

I. Co-Authorship Declaration
I hereby declare that this thesis incorporates material that is result of joint
research, as follows:
This thesis incorporates the outcome of a joint research undertaken in
collaboration with Kush Aggarwal under the supervision of Dr. Jill Urbanic. The
collaboration is covered in Chapter 9 of the thesis. In all cases, the key ideas,
primary contributions, experimental designs, data analysis and interpretation, were
performed by the author, and the contribution of co-authors was primarily through
the provision of comments, thoughts, and suggestions for improvement to the
quality of work.
I am aware of the University of Windsor Senate Policy on Authorship and I
certify that I have properly acknowledged the contribution of other researchers to
my thesis, and have obtained written permission from each of the co-author(s) to
include the above material(s) in my thesis.
I certify that, with the above qualification, this thesis, and the research to
which it refers, is the product of my own work.

II. Declaration of Previous Publication
This thesis includes concepts, methodology, and excerpt(s) from 2 original
papers that have been previously published in peer reviewed journals, as follows:

Thesis Chapter Publication title/full citation Publication
status*
Chapter(s)  Aggarwal, L., Urbanic, R., and Aggarwal, Published
1,2,5,8 K., "A Reconfigurable Algorithm for
Identifying and Validating Functional
Workspace of Industrial Manipulators,"
SAE Technical Paper 2014-01-0734, 2014,
doi:10.4271/2014-01-0734.

iii
Chapter(s)  Aggarwal, L., Aggarwal, K., and Urbanic, Published
2,6,8,10 R. J. (2014). Use of artificial neural
networks for the development of an inverse
kinematic solution and visual identification
of singularity zone(s). 47th CIRP
Conference on Manufacturing Systems.
Windsor: Elsevier Ltd. doi:
10.1016/j.procir.2014.01.107

I certify that I have obtained a written permission (Appendices E, F) from
the copyright owner(s) to include the above published material(s) in my thesis. I
certify that the above material describes work completed during my registration as
graduate student at the University of Windsor.
I declare that, to the best of my knowledge, my thesis does not infringe
upon anyone’s copyright nor violate any proprietary rights and that any ideas,
techniques, quotations, or any other material from the work of other people
included in my thesis, published or otherwise, are fully acknowledged in
accordance with the standard referencing practices. Furthermore, to the extent that
I have included copyrighted material that surpasses the bounds of fair dealing
within the meaning of the Canada Copyright Act, I certify that I have obtained a
written permission from the copyright owner(s) to include such material(s) in my
thesis.
I declare that this is a true copy of my thesis, including any final revisions,
as approved by my thesis committee and the Graduate Studies office, and that this
thesis has not been submitted for a higher degree to any other University or
Institution.

iv
ABSTRACT
Automation has led to industrial robots facilitating a wide array of high speed,
endurance, and precision operations undertaken in the manufacturing industry
today. An acceptable level of functioning and control is therefore vital to the
efficacy and successful implementation of such manipulators. This research
presents a comprehensive analytical tool for downstream optimization of
manipulator design, functionality, and performance. The proposed model is
reconfigurable and allows for modelling and validation of different industrial
robots. Unique 3D visual models for a manipulator workspace and kinematic
singularities are developed to gain an understanding into the task space and reach
conditions of the manipulator’s end-effector. The developed algorithm also
presents a non-conventional and computationally inexpensive solution to the
inverse kinematics problem through the use Artificial Neural Networks.
Application of the proposed technique is further extended to aid in development of
path planning models for a uniform, continuous, and singularity free motion.

v
DEDICATION

To my parents,

for their unconditional love, endless support, encouragement
and
for their sacrifices in providing me with a better future.

Thank you for giving so selflessly.

vi
ACKNOWLEDGEMENTS

I would like to thank my supervisor, Dr. Jill Urbanic, for providing me with the
opportunity of pursuing this field of research under her guidance. She has been a
constant source of motivation and encouragement throughout this journey. I am
extremely thankful for her selfless dedication to both my personal and academic
development.
I owe a debt of gratitude to Dr. Ana Djuric and Dr. Jennifer Johrendt for providing
me with the tools to build a strong foundation. I am forever thankful to Dr. Djuric
for introducing me to the field of robotics. She has always made the most
challenging tasks seem so easy. I am also very thankful to Dr. Johrendt for
introducing me to the field of neural networks. She has always taken the time out
to guide me through the smallest of hurdles. I would not be where I am today if it
weren’t for Dr. Urbanic, Dr. Djuric, and Dr. Johrendt.
I would also like to acknowledge Dr. Zbignew Pasek for his guidance and
technical advice.
A special thanks goes out to my twin, Kush, who has been there with me in my
best and worst of times. I could not have asked for a better friend.
Finally, I must thank my girlfriend, Ishika, for believing in me even at times when
I doubted myself. She has always supported me in all my endeavours and has been
there for me through thick and thin. I am thankful to her for being my rock.

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TABLE OF CONTENTS
DECLARATION OF CO-AUTHORSHIP / PREVIOUS PUBLICATION ............... iii
ABSTRACT ........................................................................................................................v
DEDICATION.................................................................................................................. vi
ACKNOWLEDGEMENTS ........................................................................................... vii
LIST OF TABLES ........................................................................................................... xi
LIST OF FIGURES ........................................................................................................ xii
LIST OF APPENDICES .................................................................................................xv
LIST OF ABBREVIATIONS / SYMBOLS ................................................................. xvi
NOMENCLATURE ....................................................................................................... xix
CHAPTER 1 INTRODUCTION ......................................................................................1
1.1 Background ............................................................................................................................ 1
1.2 Research Purpose .................................................................................................................. 4
1.3 Research Limitations ............................................................................................................. 6
CHAPTER 2 LITERATURE REVIEW ..........................................................................7
2.1 Manipulator Kinematics and Modelling Techniques ....................................................... 7
2.2 Manipulator Workspace ................................................................................................. 10
2.3 Manipulator Singularity and Avoidance Techniques ..................................................... 13
2.4 Inverse Kinematics using Artificial Neural Networks .................................................... 16
CHAPTER 3 INDUSTRIAL ROBOTICS .....................................................................19
3.1 Hardware and Software ................................................................................................. 21
3.2 Symbolic Representation of Joints and Links ................................................................ 22
3.3 Manipulator Classification ............................................................................................ 23
3.4 Manipulator End-Effector Types and Application ......................................................... 25
CHAPTER 4 MATHEMATICAL CONCEPTS ...........................................................27
4.1 Degrees of Freedom (DOF) ........................................................................................... 27
4.2 Representation of Position and Orientation .................................................................. 28
4.3 Frame Transformation ................................................................................................... 29
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4.4 Roll, Pitch and Yaw (RPY) Angles ................................................................................. 31
CHAPTER 5 KINEMATIC MODELLING OF MANIPULATORS .........................33
5.1 Denavit-Hartenberg (D-H) Parameters ......................................................................... 34
5.2 Homogeneous Frame Transformations ......................................................................... 36
5.3 Joint Space ..................................................................................................................... 38
5.4 Cartesian Space ............................................................................................................. 38
5.5 Forward Kinematics ...................................................................................................... 39
5.6 Workspace and Taskspace ............................................................................................. 40
5.7 Inverse Kinematics ......................................................................................................... 41
CHAPTER 6 ARTIFICIAL NEURAL NETWORKS..................................................44
6.1 Trade-off between Generalization and Accuracy .......................................................... 44
6.2 Network Architecture ..................................................................................................... 45
6.3 Network Learning .......................................................................................................... 46
6.4 Activation Function ........................................................................................................ 47
6.5 Data Pre-Processing and Post Processing .................................................................... 50
6.6 Division of Data ............................................................................................................. 50
6.7 Network Prediction Capability ...................................................................................... 51
6.8 Inverse Kinematics using Artificial Neural Networks .................................................... 52
6.8.1 Challenges in developing an ANN Architecture ........................................................ 53
6.8.2 Generalization and Accuracy of the ANN Model ....................................................... 55
CHAPTER 7 JACOBIAN: VELOCITY KINEMATICS ............................................59
7.1 Newton Euler Recursive Method.................................................................................... 59
7.2 Wrist Partitioned Manipulators ..................................................................................... 62
CHAPTER 8 KINEMATIC SINGULARITIES ...........................................................64
8.1 Types of Singularities ..................................................................................................... 67
8.2 Singularity Free Geometric Path Planning ................................................................... 69
CHAPTER 9 RECONFIGURABLE MODEL ..............................................................74
CHAPTER 10 CASE STUDIES AND RESULTS ........................................................79
10.1 6 DOF Industrial Robot: FANUC M16iB/20 ................................................................. 80
10.2 6 Axis CNC Machine ...................................................................................................... 84
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