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Studies in Autonomous Ground Vehicle Control Systems: Structure and Algorithms STUDIES IN AUTONOMOUS GROUND VEHICLE CONTROL SYSTEMS: STRUCTURE AND ALGORITHMS DISSERTATION Presented in Partial Ful¯llment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Qi Chen, B.S.C.S.E, M.S. ***** The Ohio State University 2007 Dissertation Committee: Approved by UmitÄ OzgÄuner,AdviserÄ Keith Redmill Adviser Eylem Ekici Graduate Program in Electrical and Computer Engineering °c Copyright by Qi Chen 2007 ABSTRACT This dissertation studies issues in the implementation of the control system and path-planning algorithms in autonomous ground vehicles (AGV), which are designed for and further developed from both DARPA Grand Challenges in 2004 and 2005. A hierarchical generic control architecture of the AGV control system is presented. In this architecture, the functionalities required for an AGV control system are de- composed into small and well de¯ned tasks which are then carried out by di®erent controllers. As an example, ION, the Intelligent O®-road Navigator, the autonomous vehicle participated in DARPA Grand Challenge 2005, is then introduced. Its control system structure and the hierarchical task decomposition are studied and designed. The discrete-event state machine in ION's navigation module and its physical layout of the vehicle hardware is also discussed. In order to safely, smoothly and e±ciently guide the vehicle to the goals, the navigation module in the control system is a very important part. The path-planning algorithms applied in ION's navigation module are then elaborated. We develop a real-time path-planning algorithm utilizing fuzzy logic. Two di®erent fuzzy controller, fuzzy steering controller and fuzzy speed controller, are designed and discussed to ensure the planned path is goal-reaching and obstacle-free. In addition, because of the vehicle's physical constraints, we study the smooth path planning problem for the autonomous ground vehicles and the quartic spline interpolation method is ii proposed. In order to make sure that the distance between the line segments of the given waypoints and the smooth path is small so that the vehicle stays right on the path, we formulate and study the minimum o®set smooth interpolation problem. With the given way points and maximum turning curvature, the minimum o®set can be calculated by using quartic splines. In the end we consider the maneuver planning problem. The vector space and state flow of the nonholonomic systems are de¯ned. Three di®erent approaches, Lie algebra approximation, dynamic programming, and nonlinear control method, are presented for di®erent applications. iii To Lu and Albert iv ACKNOWLEDGMENTS I would like to express my sincere gratitude to my adviser, Dr. UmitÄ OzÄuner,Ä for his guidance and continuous support throughout my study. Especially, I want to thank him for providing me the precious opportunity to participate in the ¯rst and second DARPA Grand Challenge, as well as the advise and encouragement throughout these events. I would also like to thank Dr. Eylem Ekici and Dr. Keith Redmill for serving in my dissertation committee. I want to thank Dr. Keith Redmill, John Martin, Dr. Charles Toth, Matt Knoll- man, George McWilliams, Rong Xu, Dr. Zhiyu Xiang, Yongjie Zhu, and everyone who worked and tested Terremax and ION in the Nevada desert. It is my pleasure to work with them. And I've learned a great deal from everyone. I would like to acknowledge the ¯nancial support from the DARPA MICA program via AFRL, the Collaborative Center of Control Science and the Ohio State University. Finally, I would like to thank all my friends and family. My special appreciation goes to my wife, Lu. v VITA October 22, 1975 . Born - Wenlin, Zhejiang, China June 1997 . B.S. in Control Science & Engineering College of Info. Science & Engineering Zhejiang University Hangzhou, China January 2000 . M.S. in Control Science & Engineering College of Info. Science & Engineering Zhejiang University Hangzhou, China 2000-2002 . Project Manager/Senior Engineer Shanghai Research Institute of Huawei Technologies Shanghai, China 2002 - 2006 . Graduate Research Associate Dept. of Elec. & Computer Eng. The Ohio State University Columbus, OH PUBLICATIONS Research Publications Q. Chen and U.Ä OzgÄuner,\IntelligentÄ O®-road Navigation Algorithms and Strategies of Team Desert Buckeyes in the DARPA Grand Challenge '05," in Journal of Field Robotics, Volume 23, Issue 9, pp. 729-743, Sept. 2006. vi Q. Chen and U.Ä OzgÄuner,\Real-TimeÄ Navigation for Autonomous Vehicles: A Fuzzy Obstacle Avoidance and Goal Approach Algorithm," in Proceedings of the 24th Amer- ican Control Conference, Portland, Oregon, June 2005. Q. Chen and U.Ä OzgÄunerandÄ K. Redmill, \Ohio State University at the 2004 DARPA Grand Challenge: Developing a Completely Autonomous Vehicle," in Intelligent Systems, IEEE, Volume: 19, Issue: 5, pp. 8 - 11, Sept.-Oct. 2004. Y. Hai and Q. Chen and U.Ä OzgÄuner,Ä \Control System Architecture for TerraMax - The o®-road intelligent navigator," in the 5th IFAC Symposium on Intelligent Autonomous Vehicles, Lisbon Portugal, July 2004. Q. Chen and U.Ä OzgÄuner,\AÄ Hybrid System Model and Overlapping Decomposition for Vehicle Flight Formation Control," in Dynamics of Continuous, Discrete and Impulsive Systems, Series B: Applications and Algorithms, Volume: 11, Number: 4-5, pp. 475{488, 2004. Q. Chen and U.Ä OzgÄuner,,\AÄ Hybrid System Model and Overlapping Decomposition for Vehicle Flight Formation Control," in Proceedings of the 42th IEEE Conference on Decision and Control, Maui, Hawaii, pp. 516 - 521, Dec., 2003. Q. Chen and X. Jin and S. Wang, \Application of Advanced Process Control in Aromatic Rectifying Tower," in Information and Control (Chinese Journal), Volume: 28, pp. 333-337, 1999. Q. Chen and Y. Chen and S. Wang, \Computer Simulation and Application On Biochemical Process," in Computer Simulation (Chinese Journal), Volume: 16, No. 1, pp. 39-41, 1999. Q. Chen and N. Wang and S. Wang, \An Improved Multi-layer ANN Learning Algo- rithm," in Control Theory, Technology and Application (Chinese Journal), Volume: 4, 1997. FIELDS OF STUDY Major Field: Electrical and Computer Engineering vii TABLE OF CONTENTS Page Abstract . ii Dedication . iv Acknowledgments . v Vita . vi List of Tables . xi List of Figures . xii Chapters: 1. Introduction . 1 1.1 Literature in Autonomous Ground Vehicle Control Structure . 2 1.2 Literature in Path-Planning Problems . 4 1.3 Literature in Nonholonomic System Path-Planning Problems . 8 1.4 The DARPA Grand Challenge Overview . 10 1.5 Dissertation Outline . 13 2. Generic Architecture for Autonomous Ground Vehicle Control Systems . 14 2.1 Introduction . 14 2.2 General Requirements for AGV Control System . 15 2.3 The Hierarchical Generic Architecture . 16 2.4 Layer Descriptions . 18 2.4.1 Operational Level . 18 2.4.2 Skill Level . 19 2.4.3 Tactical Level . 21 viii 2.4.4 Strategic Level . 24 2.5 Cooperations Among AGVs in the Generic Architecture . 26 3. ION's Control System Structure . 29 3.1 The Physical Layout and Functional Architecture of Vehicle Hardware 29 3.2 Hierarchical Task Decomposition . 33 3.3 The Discrete-Event State Machine in the Navigation Module . 33 4. A Real-Time Navigation Algorithm for Autonomous Ground Vehicles . 38 4.1 Introduction . 38 4.1.1 Concerns on the Navigation Module . 38 4.1.2 Concerns on Obstacle Avoidance Algorithms . 40 4.2 Fuzzy Steering controller . 42 4.2.1 Basic Steering Controller . 45 4.2.2 Focus Rule . 49 4.2.3 Focus-vs-Search Rule . 53 4.2.4 Persistence Rule . 55 4.2.5 Brief Summary of the Fuzzy Steering Controller . 56 4.3 Fuzzy Speed Controller . 61 4.3.1 The Anticollision Rule . 61 4.3.2 Safe-Turning Rule . 63 4.4 Performance of the Navigator . 64 4.4.1 Simulation . 64 4.4.2 Tests on Small Robots . 69 4.4.3 Implementation in ION . 69 4.5 Conclusion . 72 5. Smooth Path Planning for Car-Like Autonomous Ground Vehicles . 73 5.1 Introduction . 73 5.1.1 Preliminaries on Plane Curves . 74 5.1.2 Smooth Path-Planning Problem Setup . 76 5.2 Smooth Path Planning . 78 5.2.1 Polynomial Piecewise G2-Interpolation . 78 5.2.2 Quartic Piecewise G2-Interpolation . 79 5.2.3 Properties of the Quartic Splines . 81 5.2.4 Quartic Splines Examples . 84 5.3 Optimal Quartic Splines . 86 5.4 Concluding Remarks . 91 ix 6. Maneuver Automation for Car-like Ground Vehicles . 92 6.1 Introduction . 92 6.2 Simpli¯ed Car-Like Vehicle Model . 93 6.2.1 Kinematic Model . 93 6.2.2 The Vector Fields and the State Flow . 94 6.2.3 The Unit Operations and Lie Algebra Operation . 96 6.3 Maneuver Automation Problem Setup . 98 6.4 Direct Approach by Using Lie Algebra Tools . 100 6.5 Dynamic Programming Method . 101 6.5.1 Formulating the Planning Process . 101 6.5.2 Rolling Horizon Approximation . 102 6.5.3 Best First Search . 103 6.6 Nonlinear Controller . 104 6.7 Conclusion . 106 7. Conclusions . 109 7.1 Summary and Contribution . 109 7.2 Future Research Directions . 111 Bibliography . 112 x LIST OF TABLES Table Page 4.1 The notation list . 43 4.2 The selection rule . 46 xi LIST OF FIGURES Figure Page 2.1 The generic architecture for a class of AGV control systems. 17 2.2 Abstract hierarchy structure of the generic architecture. 18 2.3 The cooperative interaction among agents with hierarchical structure and with other agents. 1) Human operator controls the mobile agents through a centralized station agent; or, the station agent dispatches plans to the strategic level of each agents, in a centralized cooperation way; 2) Agents exchanges states and plans in the strategic and tactical level, and negotiates in a decentralized cooperation way; 3) Agents and human drivers \cooperate" under prede¯ned rules.
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