AUTONOMOUS AND COOPERATIVE MULTI-UAV GUIDANCE IN ADVERSARIAL ENVIRONMENT

215 Pages·2007·7.21 MB·English

AUTONOMOUS AND COOPERATIVE MULTI-UAV GUIDANCE IN
ADVERSARIAL ENVIRONMENT
by
UGUR ZENGIN
Presented to the Faculty of the Graduate School of
The University of Texas at Arlington in Partial Fulfillment
of the Requirements
for the Degree of
DOCTOR OF PHILOSOPHY
THE UNIVERSITY OF TEXAS AT ARLINGTON
May 2007
Copyright (cid:13)c by UGUR ZENGIN 2007
All Rights Reserved
To my family for their support and making me who I am.
ACKNOWLEDGEMENTS
I would like to thank several people who made this research possible. First, I would
like to acknowledge the guidance of my supervising professor Dr. Atilla Dogan during
my PhD studies. Our frequent meetings and fruitful discussions, thanks to his immense
availability, have been very beneficial. It was only due to his continuous encouragement
thatIcouldpublishseveraljournalandconferencepapersduringmystudies. Iwouldalso
like to thank him for the moral and financial support he had offered, without which, it
would have been very challenging for me to maintain my focus consistently on education
and research.
I would also like to thank my dissertation committee members Dr. Don Wilson,
Dr. Frank Lewis, Dr. Kamesh Subbarao and Dr. Wen Chan, for their constructive
comments and for taking time to serve in my dissertation defense committee. I especially
would like to thank to Dr. Subbarao for teaching me several courses and being available
for any kind of discussion during my years at UT Arlington.
I wish to thank the Department of Mechanical and Aerospace Engineering at UT
Arlington for the financial support provided to me for my conference paper presenta-
tions in conferences. I am also grateful to Ms. Barbara Howser, the subject librarian of
Aerospace Engineering for her immense help in conducting literature survey and obtain-
ing research information from various sources.
I am grateful to all the teachers who have taught me from elementary school to
college,inTurkey. IwouldalsoliketothankformerandcurrentlabmatesoftheComputer
Aided Control System Design Lab, especially Sriram Venkataramanan (dear bro) for his
continuous encouragement, even from overseas, and Eunyoung Kim for sharing her food
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with me in my endless nights in the lab. My many other friends have been a wonderful
community during my UTA years, providing support and entertainment, especially the
Turkish community and UTA Soccer Club team members.
Lastly, I am extremely grateful to my family for their continuous support, encour-
agement and patience. This dream would have never came true without them.
April 20, 2007
v
ABSTRACT
AUTONOMOUS AND COOPERATIVE MULTI-UAV GUIDANCE IN
ADVERSARIAL ENVIRONMENT
Publication No.
UGUR ZENGIN, Ph.D.
The University of Texas at Arlington, 2007
Supervising Professor: Dogan, Atilla
The research presented in this dissertation is aimed at developing rule-based au-
tonomous and cooperative guidance strategies for UAVs to perform missions such as path
planning, target tracking and rendezvous while reducing their risk/threat exposure level,
and avoiding threats and/or obstacles by utilizing measurement information provided by
sensors.
First, a mathematical formulation is developed to represent the area of operation
that contains various types of threats, obstacles, and restricted areas, in a single frame-
work. Once constructed, there will be no need to distinguish between threats, obstacles
and restricted areas as the framework already contains the information on what needs
to be avoided and the level of penalty for a given position in the area. This framework
provides the mathematical foundation for the guidance strategies to make intelligent de-
cisions during the execution of the mission and also provides scalar metrics to assess the
performance of a guidance strategy in a given mission.
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The autonomous guidance strategies are developed by using a rule-based expert
system approach with the requirements of completing assigned mission or task, avoiding
obstacle/restricted-areas, minimizing threat exposure level, considering the dynamic and
communication constraints of the UAVs and avoiding collision. All these requirements
and objectives are quantified and prioritized to facilitate the development of guidance
algorithms that can be executed in real–time. The strategies consist of a set of “decision
states”, which contain rules to determine how the host UAV should move by generating
heading and speed signals.
Cooperation of multiple UAVs is modeled by minimizing a cost function, which
is constructed based on the level of threat exposure for each UAV and distance of each
UAV relative to the target. This improves the performance of the system in the terms
of increasing the total area of coverage of the sensors onboard the UAVs, increasing the
flexibilityoftheUAVstosearchforbettertrajectoriesintermsofobstacle/restricted–area
avoidance and threat exposure minimization, and improving the estimation by providing
additional sources of measurement.
Finally,theperformancesofthealgorithmsareevaluatedinaMATLAB/Simulink(cid:13)c
based simulation environment, which includes the dynamics of each vehicle involved, the
models of sensor measurement and data communication with different sampling rates,
andthediscreteexecutionofthealgorithms. Thesimulationresultsdemonstratethatthe
proposed algorithms successfully generates the trajectories that satisfy the given mission
objectives and requirements.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi
CHAPTER
1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Introduction and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.2 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Dissertation Layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2. LITERATURE SEARCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1 Autonomous Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Adversarial Environments and Map Building . . . . . . . . . . . . . . . . 11
2.3 Path Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.1 Single Vehicle Path Planning . . . . . . . . . . . . . . . . . . . . 13
2.3.2 Multi Vehicle Path Planning . . . . . . . . . . . . . . . . . . . . . 16
2.4 Target Pursuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.4.1 Pursuit-Evasion Games . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4.2 Ground target tracking and detection . . . . . . . . . . . . . . . . 20
2.4.3 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
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2.5 Cooperative Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3. FORMULATION OF ADVERSARIAL ENVIRONMENT . . . . . . . . . . . 25
3.1 Formulation of Area of Operation . . . . . . . . . . . . . . . . . . . . . . 25
3.2 Probabilistic Threat Exposure Map (PTEM) . . . . . . . . . . . . . . . . 28
3.3 Time-Variant Probabilistic Threat Exposure Map . . . . . . . . . . . . . 30
3.4 Regions by the Level of Threat Exposure . . . . . . . . . . . . . . . . . . 32
3.5 Gradient Search on PTEM . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.6 Minima of PTEM on a Closed Disk . . . . . . . . . . . . . . . . . . . . . 34
3.7 Computational Efficiency of Evaluating PTEM . . . . . . . . . . . . . . . 37
4. TRAJECTORY PLANNING ALGORITHM . . . . . . . . . . . . . . . . . . . 39
4.1 Trajectory Planning Strategy (TPS) . . . . . . . . . . . . . . . . . . . . 39
4.1.1 Dynamic Constraints of the UAV . . . . . . . . . . . . . . . . . . 40
4.1.2 Strategy Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2 Comparison with the other strategies . . . . . . . . . . . . . . . . . . . . 48
5. ESTIMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.1 Estimation via heading and speed . . . . . . . . . . . . . . . . . . . . . . 57
5.2 Estimation via position . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.3 Comparison of the estimation algorithms . . . . . . . . . . . . . . . . . . 62
5.4 Data Fusion and Estimation . . . . . . . . . . . . . . . . . . . . . . . . . 63
6. POINT SEARCH GUIDANCE ALGORITHM . . . . . . . . . . . . . . . . . . 68
6.1 Target Following Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . 68
6.1.1 Restricted Area Search . . . . . . . . . . . . . . . . . . . . . . . . 71
6.1.2 Strategy Constraints . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.1.3 Strategy cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.2 Simulation Environment and Results . . . . . . . . . . . . . . . . . . . . 81
6.2.1 Modular Structure of the Simulation Environment . . . . . . . . . 81
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6.2.2 Implementation of the Strategy and Simulation Results . . . . . . 82
7. GRADIENT SEARCH GUIDANCE ALGORITHM . . . . . . . . . . . . . . . 98
7.1 Target Following Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . 98
7.1.1 Preliminary Definitions . . . . . . . . . . . . . . . . . . . . . . . . 99
7.1.2 Decision Factors and Strategy States . . . . . . . . . . . . . . . . 104
7.1.3 Computation of Desired Heading and Admissible Range . . . . . 108
7.1.4 Computation of Desired Speed and Admissible Range . . . . . . . 114
7.1.5 Scheduling Scheme for Heading Difference Constraint . . . . . . . 117
7.1.6 Detection of Local Minima . . . . . . . . . . . . . . . . . . . . . . 118
7.2 Simulation Results and Comparison with the Point Search Guidance . . . 119
7.3 Feasibility and Implementation of the Algorithm . . . . . . . . . . . . . . 129
8. COOPERATIVE GUIDANCE ALGORITHM . . . . . . . . . . . . . . . . . . 131
8.1 Cooperative Target Following Strategy . . . . . . . . . . . . . . . . . . . 131
8.1.1 Formulation of Cooperation . . . . . . . . . . . . . . . . . . . . . 132
8.1.2 Implementation of Cooperative Strategy . . . . . . . . . . . . . . 135
8.1.3 Improving Computational Efficiency . . . . . . . . . . . . . . . . 136
8.1.4 Collision Avoidance . . . . . . . . . . . . . . . . . . . . . . . . . . 138
8.1.5 Guidance Strategy for Each Individual UAV . . . . . . . . . . . . 140
8.2 Simulation Environment and Results . . . . . . . . . . . . . . . . . . . . 142
8.2.1 Modular Structure of the Simulation Environment . . . . . . . . . 142
8.2.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . 145
9. VIRTUAL TARGET TRACKING . . . . . . . . . . . . . . . . . . . . . . . . 158
9.1 Trajectory planning via virtual target tracking . . . . . . . . . . . . . . . 159
9.2 Rendezvous via virtual target tracking . . . . . . . . . . . . . . . . . . . 167
10.CONCLUSIONS AND FUTURE WORK . . . . . . . . . . . . . . . . . . . . 170
10.1 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 170
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