by Souma M. Alhaj Ali

238 Pages·2004·2.2 MB·English

by  Ayman Abu Hammad

Technologies for Autonomous
Navigation in Unstructured Outdoor Environments
A dissertation submitted to the
Division of Research and Advanced Studies
of the University of Cincinnati

in partial fulfillment of the
requirements for the degree of

DOCTORATE OF PHILOSOPHY

In the Department of Mechanical, Industrial and Nuclear Engineering
of the College of Engineering

2003

by
Souma M. Alhaj Ali

B.S. in Industrial Engineering, University of Jordan, 1995
M.S. in Industrial Engineering, University of Jordan, 1998

Committee Chair: Dr. Ernest L. Hall
ABSTRACT

Robots have been used in manufacturing and service industries to improve productivity,
quality, and flexibility. Robots are usually mounted on a fixed plate, or on rails, and can move in
a limited manner. The success of robots in these environments encourages the use of mobile
robots in other applications where the environments are not structured, such as in outdoor
environments.
This dissertation presents the development of an autonomous navigation and obstacle
avoidance system for a wheeled mobile robot (WMR) operating in an unstructured outdoor
environments. The algorithm produces the robot’s path positioned within the road boundaries
and avoids any fixed obstacles along the path. The navigation algorithm was developed from a
feedforward multilayer neural network. The network used a quasi-Newton backpropagation
algorithm for training.
Proportional derivative computed-torque, proportional, integral, derivative computed-
torque, digital, and adaptive controllers were developed to select suitable control torques for the
motors, which cause the robot to follow the desired path from the navigation algorithm.
Simulation software permitting easy investigation of alternative architectures was
developed by using Matlab and C++. The simulation software for the controllers was developed
for two case studies. The first case study is the two-link robot manipulator, and the second is a
navigation controller for the WMR. The simulation software for the WMR navigation controller
used the Bearcat III dynamic model, developed in this dissertation.
Simulation results verify the effectiveness of the navigation algorithm and the controllers.
The navigation algorithm was able to produce a path with a small mean square error, compared
to the targeted path, which was developed by using an experienced driver. The algorithm also
produced acceptable results when tested with different kinds of roads and obstacles. The
controllers found suitable control torques, permitting the robot to follow these paths. The digital
controller produced the best results.
The significance of this work is the development of a dynamic system model and
controllers for WMR navigation, rather than robot manipulators, which is a new research area. In
addition, the navigation system can be utilized in numerous applications, including various
defense, industrial and medical robots.

To:
my deceased father,
my mother,
my husband, Ayman,
and my sons, Mahmoud and Omar
ACKNOWLEDGEMENTS

The author wish to express her appreciation and gratitude towards her advisor: Dr. Ernest
L. Hall for his support and guidance. His continued guidance has helped the author in directing
this dissertation towards achieving valuable contributions. The author conveys her heartiest
thanks to the other dissertation committee members: Dr. Richard L. Shell, Dr. Issam A.
Minkarah, Dr. Ronald L. Huston, and Dr. Thomas R. Huston for their continued support that
enabled the student to achieve the objectives of his research.
My gratitude to all who provided me with continued support: my mother, husband, brothers and
sisters.
Table of Contents

Chapter 1: Introduction….…………………………………………………………..
1.1 Introduction…………………………………………………………………
1.2 Research Goals……………………………………………………………...
1.3 Significance…………………………………………………………………
1.4 Contribution and relation to the present state of knowledge in the field…...
1.5 Methodology………………………………………………………………..
Chapter 2: Background….…………………………………………………………..
2.1 Introduction…………………………………………………………………
2.2 Navigation…………………………………………………………………..
2.2.1 Systems and Methods for mobile robot navigation………………………
2.2.1.1 Odometry and Other Dead-Reckoning Methods……………………….
2.2.1.2 Inertial Navigation………………………………………………............
2.2.1.3 Active Beacon Navigation Systems……………………………………..
2.2.1.4 Landmark Navigation…………………………………………………...
2.2.1.5 Map-based Positioning…………………………………………………..
2.2.1.6 GPS……………………………..……………………………………….
2.3 Literature Review...…………………………………………………………
2.3.1 Vision and sensor based navigation..……………………………………..
2.3.2 Use of fuzzy logic..……………………………………………………….
2.3.3 Use of artificial neural networks……………………………………….....
2.3.4 Use of neural integrated fuzzy controller…………………………………
2.3.5 Map-based navigation..…………………………………………………...
2.3.6 Biological navigation……………………………………………………..
2.3.7 Some new methods are proposed…………………………………………
2.3.8 Navigation in unstructured environment……………………………….....
2.4 Controllers for mobile robots autonomous navigation……………………..
2.5 Defense Robot………………………………………………………………
2.5.1 Remote-controlled robots…………………………………………………
2.5.2 Autonomous Robots………………………………………………………
2.5.3 Other research trends in defense robot …………………………………...
Chapter 3: Robot modeling….………………………………………………………
3.1 Introduction….……………………………………………………………...
3.2 Robot kinematics….………………………………………………………...
3.2.1 Robot description..………………………………………………………..
3.2.2 Posture kinematic model………………………………………………….
3.2.3 Configuration kinematic model..…………………………………………
3.3 Robot dynamics using Lagrange formulation………………………………
3.3.1 Configuration dynamic model..…………………………………………..
3.3.2 Posture dynamic model..………………………………………………….
3.4 Robot dynamics using Newton-Euler method……………………………...
3.4.1 Dynamic analysis…………………………………………………………
3.4.2 Calculation of the Pseudo-inverse matrix (Moore-Penrose generalized
inverse)……………………………………………………………………
3.4.3 Properties of the dynamic model…………………………………………
3.4.4 Bearcat III dynamic model………………………………………………..
3.4.4.1 Calculation of the moment of inertia……………………………………
3.4.4.2 Calculation of the resultant normal force f ……………………………
n
Chapter 4: ANN……………………………………………………………………
4.1 Introduction...……………………………………………………………….
4.2 ANN Mathematical model………………………………………………….
4.3 ANN learning rules…………………………………………………………
4.3.1 Supervised learning……………………………………………………….
4.3.2 Reinforcement learning…………………………………………………...
4.3.3 Unsupervised learning…………………………………………………….
Chapter 5: The navigation system…………………………………………………...
5.1 Introduction…………………………………………………………………
5.2 The sensory devices………………………………………………………...
5.3 Navigation algorithm……………………………………………………….
5.3.1 ANN architecture…………………………………………………………
5.3.1.1 ANN input……………………………………………………………….
5.3.1.2 ANN output……………………………………………………………...
5.3.1.3 ANN layers architecture…………………………………………………
5.3.2 ANN training……………………………………………………………...
5.3.3 Results…………………………………………………………………….
5.3.4 Conclusion………………………………………………………………..
Chapter 6: Dynamic simulation for real time motion control of the robot………….
6.1 Introduction…………………………………………………………………
6.2 CT controllers………………………………………………………………
6.2.1 PD CT controller………………………………………………………….
6.2.2 PID CT controller…………………………………………………………
6.2.3 Simulation for the PD CT controller for the two-link manipulator………
6.2.3.1 Simulation architecture………………………………………………….
6.2.3.2 Simulation results………………………………………………………..
6.2.4 Simulation of the PD CT controller for the WMR navigation……………
6.2.4.1 Simulation architecture………………………………………………….
6.2.4.2 Simulation results………………………………………………………..
6.2.4.3 Conclusion………………………………………………………………
6.2.5 Simulation for the PID CT controller for the two-link manipulator……...
6.2.5.1 Simulation architecture………………………………………………….
6.2.5.2 Simulation results………………………………………………………..
6.2.6 Simulation for the PID CT controller for the WMR navigation………….
6.2.6.1 Simulation architecture………………………………………………….
6.2.6.2 Simulation results………………………………………………………..
6.2.6.3 Conclusion………………………………………………………………
6.3 Digital controller……………………………………………………………
6.3.1 Simulation of the digital controller for the two-link manipulator………...
6.3.2 Simulation of the digital controller for the WMR motion………………..
6.3.2.1 Conclusion………………………………………………………………
6.4 Approximation-based adaptive controller…………………………………..
6.4.1 The tracking problem……………………………………………………..
6.4.2 Error Dynamics………………………………………………………….