UNMANNED AERIAL VEHICLE REAL-TIME GUIDANCE SYSTEM VIA

237 Pages·2007·3.75 MB·English

UNMANNED AERIAL VEHICLE REAL-TIME GUIDANCE
SYSTEM VIA STATE-SPACE
HEURISTIC SEARCH

MANUEL SOTO
Department of Electrical & Computer Engineering

APPROVED:

Patricia A. Nava, Ph.D., Chair
Benjamin C. Flores, Ph.D.
Jack F. Chessa, Ph.D.
Thompson Sarkodie-Gyan, Ph.D.

Pablo Arenaz, Ph.D.
Dean of the Graduate School
Copyright
by
Manuel Soto
2007
Dedication
I dedicate this Master’s Thesis to those who supported me in this effort. First and foremost:
Thank you God. Second, my eternal gratitude goes to all my family: to all of my friends, cousins,
aunts, uncles, sisters, mother, and father. To all of you; I love you and I thank you for your
encouragement, for your patience, for your advice, and most of all for your prayers.

To Veronica—my beautiful lady; thanks for caring and for loving me. To my numerous cousins:
your guidance and your being there for me are always greatly appreciated. To my numerous
aunts and uncles: thank you for sharing your wisdom and experience, and thank you for giving
me your advice. To all my friends and family; thank you for all your help, support,
encouragement, and prayers.

Dad; had you not taught me to work hard, to use my brain, and to do quality work, I don’t know
if I would be an engineer today; I don’t know if I would have completed this advanced degree;
and I don’t know if I would enjoy working in my profession as much as I do. Thanks again Dad.
Lastly, to my God in Heaven, who sits next to His Son, Jesus, and who blesses us with His Holy
Spirit: Thank You.
UNMANNED AERIAL VEHICLE REAL-TIME GUIDANCE
SYSTEM VIA STATE SPACE
HEURISTIC SEARCH

by

MANUEL SOTO, B.S.E.E.

THESIS

Presented to the Faculty of the Graduate School of
The University of Texas at El Paso
in Partial Fulfillment
of the Requirements
for the Degree of

MASTER OF SCIENCE

Department of Electrical & Computer Engineering
THE UNIVERSITY OF TEXAS AT EL PASO
December 2007
Acknowledgements
I would like to thank both Dr. Patricia A. Nava and Mr. Luis E. Alvarado for their effort
and support in this project which began as a research study and has evolved to a real-world
system. Their guidance, collaboration, and mentoring support has yielded a publication in the
American Institute of Aeronautics and Astronautics, a presentation at the National Defense and
Industrial Association conference, and a real-time guidance system that is currently undergoing
empirical testing on DFCS. To Mr. Luis E. Alvarado, I would like to express my sincere thanks
and gratitude for entrusting me to this project, and to Dr. Patricia A. Nava, I would also like to
express my sincere thanks and gratitude to her for her acceptance, guidance and feedback on this
project. I feel blessed that I was provided with such excellent mentors.
My DFCS team, consisting both of Government contractors and of Government
personnel, has my appreciation for the help they provided in defining, designing and developing
the components for making this system realizable. The Government contractors, UNITECH and
Proteus, and the RO-CR-C Government personnel greatly assisted in providing the much
required projects needs, which were used to develop the product specifications and its design.
Arron Hardesty: thanks for writing the UDP class that links the RTPP to DFCS. Dr. John D.
Medrano: thank you for helping create the dynamic array class. Lupita Soliz: thank you for the
explanations about an algorithm’s running time. Gary Montana: thanks for allowing me to bench
test the console computer prototype; it was very useful for both running and developing the TMC
and the RTPP. Dan Cullen: thanks for helping me get this project started. Pat Kearny: thank you
for explaining terrain masking. Paul Berger: thank you for the discussions about the different
coordinate systems for representing the surface of WSMR. Gary Hakenson: thanks for providing
the equipment for linking the RTPP to the DFCS real-time computer network. Myron C. Moore:
thanks for proofreading the AIAA manuscript. Eugene Lawson: thanks for your assistance in
helping me design the RTPP graphics.
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I would also like to thank the NewTech engineering team for their assistance; Perry
Hutchinson and Jeff Brickley: thank you for helping me to understand OpenGL and DTED.
To my Thesis Committee: Dr. Benjamin C. Flores, Dr. Jack F. Chessa, Dr. Patricia A.
Nava, and Dr. Thompson Sarkodie-Gyan—thank you for reviewing my work.
To my employer, UNITECH, whose tuition reimbursement support helped to make
graduate school less costly for me, I am grateful. I am also eternally grateful to two individuals
whose support and encouragement, both personal and professional, carried me through this
project and through the toughest times of graduate school: Herbert Saxman and Guadalupe
Arellano. Finally, to my father, Modesto Soto, I would like to express my love and gratitude for
his advice, help, and support in this Thesis, my previous educational endeavors, and in life.
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Abstract
This Thesis focuses on the research, implementation, and empirical testing of a real-time
(RT) Unmanned Aerial Vehicle (UAV) guidance system—the Real-Time Path Planner (RTPP).
The RTPP was created for the following reasons: (1) proof-of-concept, i.e., to verify the real-
world feasibility of developing a real-time guidance system by using AI techniques; (2) bench-
marking, i.e., to create a platform for using the most promising Artificial Intelligence (AI)
theories; (3) to show that a real-time guidance system is beneficial to existing Department of
Defense (DoD) Target Command and Control Systems (TCCS). In this study, the RTPP
undergoes empirical testing on an operational DoD TCCS called the Drone Formation Control
System (DFCS). This testing is conducted by linking the RTPP to the DFCS real-time computer
network. The RTPP provides flight-patterns to be used in 6-Degree Of Freedom (DOF) flight
simulations by the DFCS Guidance, Navigation, and Control (GNC) systems.
At present, guidance systems used by the DoD targets community in missions (i.e. the
remote operation of aerial vehicles) require specially trained personnel to develop flight-patterns
(i.e. precisely defined flight trajectories) prior to conducting missions. This process is
constrictive and static because it forces missions to be performed as specified by these pre-
launch generated flight-patterns. These limitations make missions extremely inflexible. This
study proposes a solution that alleviates this problem. The solution is a real-time guidance
system that allows DoD TCCS personnel (e.g. the project engineer) to make changes to flight
profiles in real-time.
The RTPP is a prototype of this solution. It provides a user-friendly computer interface
for mission operators to safely guide and control target presentation locations in real-time. This
easy-to-use interface is operated by using a mouse and keyboard to interact with its Graphical
User Interface (GUI). The mouse can be used to select the flight trajectory’s start and destination
locations on the terrain map that is displayed within the GUI. The keyboard can be used for the
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same purpose but it is required for entering the other flight trajectory parameters (e.g., those
associated with turn radius constraints).
The user-entered UAV parameters are passed to the main module of the RTPP—the A*
algorithm. Then the RTPP provides either flyable flight trajectories or no guidance at all (which
occurs only when no solution exists). The returned flight-patterns have the attributes being
flyable (i.e. minimal distance, and safe).
The RTPP can be used in obstacle rich environments, provided the obstacles are static
(e.g. mountains and other natural high elevation terrain). If the obstacles are man-made, their
locations and MSL elevations must be entered into the RTPP.
The RTPP uses files generated by the Terrain Map Creator (TMC), which is another
product of this work. The TMC is a program that queries a high resolution environmental
database for recording to a file the locations and elevations of the terrain over which the mission
will be performed.
In conclusion, the RTPP is used as the platform for testing the A* algorithm, which is the
AI theory that is the recommended as the core technological solution. Its recommendation is
verified by research, technical feasibility, analysis, empirical testing, and simulations. Hence,
this Thesis demonstrates that DoD TCCS can benefit from innovations provided by AI theory.
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Table of Contents

Acknowledgements..................................................................................................v
Abstract.................................................................................................................vii
Table of Contents...................................................................................................ix
List of Tables.......................................................................................................xiii
List of Figures......................................................................................................xiv
List of Illustrations...............................................................................................xvi
Chapter 1: Introduction............................................................................................1
1.1 Background............................................................................................1
1.1.1 Target Command and Control Systems........................................1
1.1.2 Drone Formation Control System.................................................2
1.2 Problem Statement.................................................................................3
1.3 Methodology..........................................................................................5
1.4 Evaluation Criteria.................................................................................7
1.5 Document Overview..............................................................................8
Chapter 2: Theory: Path Finding............................................................................10
2.1 Overview..............................................................................................10
2.1.1 Implications of Assumption 1.....................................................10
2.1.2 Traveling Salesperson Problem..................................................11
2.1.3 Mapping WSMR Terrain to TSP Graph.....................................12
2.1.4 Convergence of Assumptions 1 & 2...........................................13
2.2 State Space Search...............................................................................14
2.2.1 Graphs: Theory & Terminology.................................................14
2.2.2 Classic Test Problems.................................................................19
2.2.3 Search-Graph Generation from an Implicit State-Space............23
2.2.4 Algorithm Analysis.....................................................................29
2.2.5 Intelligence in Search: Human vs. Artificial...............................37
2.2.6 A* Algorithm..............................................................................40
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Chapter 3: Design, Implementation, & Integration...............................................48
3.1 Methodology........................................................................................49
3.1.1 Planning......................................................................................49
3.1.2 Development Overview..............................................................50
3.1.3 Target Platform...........................................................................51
3.2 RTPP Alpha Prototype.........................................................................52
3.2.1 GUI Object..................................................................................54
3.2.2 Graphics Display Object.............................................................57
3.2.3 Terrain Map File Processing Object...........................................65
3.2.4 A* Algorithm Object..................................................................70
3.2.5 Dynamic Array Object................................................................74
3.3 Terrain Map Creator............................................................................75
3.3.1 TMC Modules.............................................................................77
3.3.2 GUI Object..................................................................................79
3.3.3 Terrain Map File Processing Object...........................................80
3.3.4 Geographic Conversions Object.................................................82
3.3.5 NED Query Module....................................................................86
3.3.6 Quantized Terrain Maps.............................................................87
3.4 RTPP-DFCS Integration Issues...........................................................88
3.4.1 Flight Profiles..............................................................................89
3.4.2 Waypoints...................................................................................92
3.4.3 Processing the A* Path to Waypoints.........................................92
3.4.4 Bounding the RTPP Input to Guarantee Real-time Response....93
3.4.5 Problems Associated with Waypoints........................................96
3.4.6 RTPP-DFCS Interface................................................................99
3.4.7 RTPP-DFCS Integration...........................................................102
3.4.8 Guidance Position Errors..........................................................104
3.5 RTPP Beta Prototype.........................................................................105
3.5.1 Flight-Pattern Generation and Validation (FPGV) Module......107
3.5.2 Diagnostic System Module.......................................................122
3.5.3 Real-Time Timer (RTT) Module..............................................123
3.5.4 Network Link............................................................................124
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