Force Feedback and Intelligent Workspace Selection for Legged Locomotion Over Uneven Terrain
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University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School March 2019 Force Feedback and Intelligent Workspace Selection for Legged Locomotion Over Uneven Terrain John Rippetoe University of South Florida, [email protected] Follow this and additional works at: https://scholarcommons.usf.edu/etd Part of the Computer Sciences Commons, and the Robotics Commons Scholar Commons Citation Rippetoe, John, "Force Feedback and Intelligent Workspace Selection for Legged Locomotion Over Uneven Terrain" (2019). Graduate Theses and Dissertations. https://scholarcommons.usf.edu/etd/8407 This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Force Feedback and Intelligent Workspace Selection for Legged Locomotion Over Uneven Terrain by John Rippetoe A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computer Science and Engineering College of Engineering University of South Florida Co-Major Professor: Luther Palmer III, Ph.D. Co-Major Professor: Yu Sun, Ph.D. Kyle Reed, Ph.D. Rajiv Dubey, Ph.D. Stephen Deban, Ph.D. Date of Approval: March 25, 2019 Keywords: Bioinspired Robotics, Motion Planning, Terrain Classification Copyright c 2019, John Rippetoe DEDICATION This dissertation and all of the accumulated experience that went into its creation are dedicated first and foremost to God. Only through His strength, courage, and wisdom was I able to complete this work. And to my wife, best friend, and love of my life Kristen: only with your unwaivering support and love was I able to keep going through the toughest of times. I will never be able to fully express the depth of my love for you or how thankful I am that you are a part of my life. I owe you a debt of gratitude that I will never be able to repay a thousand lifetimes over. ACKNOWLEDGMENTS Throughought my time as a Ph.D. student I have receieved a great deal of support from a large number of people. All of them have, in both large and small ways, contributed to my work and its completion and I will be forever thankful for them. I would like to take the time to thank some of those people here. I would first like to thank my co-major professors, Dr. Luther Palmer, III and Dr. Yu Sun. From each of you I have learned a tremendous number of things that have made me a better student, teacher, and researcher. You both pushed me beyond what I thought I was capable of and have helped me to learn new things about myself that I know will continue to serve me well the rest of my life. I would also like to thank Dr. Kyle Reed, Dr. Stephen Deban, and Dr. Rajiv Dubey for their time and expertise as members of my committee. I have enjoyed talking with each and every one of you during our time together and wish you all the best of luck in your endevours. I would also like to thank the Department of Computer Science and Engineering and all of the wonderful staff that have kept me on track over the last 6 years. I have always felt welcome as both a student and as a member of a fantastic team and am very grateful for the support the department offered me through assistanships and teaching opportunities. The efforts of Yvette Blanchard, John Morgan, Kimberly Bluemer, Gabriela Franco, and Laura Owczarek to support every student in the department has never gone unnnoticed and was integral to my success at USF. In addition, I would also like to thank all current and former members of the Biomorphic Robotics Lab and the Robot Perception and Action Lab, including Yongqiang Huang, David Paulius, Troi Williams, Ahmad Babaeian Jelodar, MD Sirajus Salekin, Tianze Chen, Mayur Palankar, Patrick Curran, Kyle Ashley, and Joseph Cox. We have had many, many funny conversations, late nights, and good times together that I will always remember. Each of you have contributed to my work in numerous ways and have been great friends throughout the process. I know that each and every one of you will be successful in your future careers and I look forward to seeing where life takes you. Finally, I would like to acknowledge the incredible love and support of the many friends and family that have stayed by my side throughout this journey and kept me going. I am eternally grateful for my mom, Maria, and dad, Ron, for suporting me and instilling in me the many life lessons needed to complete this work. I hope that I can contine to make you proud and to one day repay you for all of the love, sweat, and tears that you have poured into me. I would also like to thank my siblings Matthew and Megan, my in-laws Roger and Tracy Suro, and friends Cole Gindhart, Joshua Murphy, Kevin Cook, Neil Ramlal, Bob Block, and Darren Selvidge. You are all a God-given blessing on my life that I could not do without. TABLE OF CONTENTS LIST OF TABLES iii LIST OF FIGURES iv ABSTRACT vii CHAPTER 1 INTRODUCTION 1 1.1 Motivation 1 1.2 Objectives 3 1.3 Contributions 4 1.4 Organization 4 CHAPTER 2 THE HEXABULL III ROBOT 6 2.1 Background 6 2.2 Hardware 11 2.3 Simulation 12 CHAPTER 3 WORKSPACE REPRESENTATION AND MOTION PLANNING 15 3.1 Background 16 3.2 Workspace Reduction 18 3.2.1 X-Z Plane 20 3.2.2 Y-Z Plane 22 3.3 Offline Body Motion Planning 26 3.4 Online Foot Trajectories 31 3.4.1 Fore-Aft 31 3.4.2 Lateral 32 3.4.3 Vertical: Position Control 33 3.4.4 Vertical: FTP Control 34 3.5 Experiments and Results 35 3.6 Conclusion 40 CHAPTER 4 FORCE MEASUREMENT 44 4.1 Introduction 44 4.2 Background 45 4.3 Force Measurement Mechanism 47 i 4.4 Experiments and Results 52 4.4.1 Complex Terrain Tests 54 4.4.2 The Influence of Slow Depress 58 4.5 Discussion 61 4.6 Conclusion 65 CHAPTER 5 TERRAIN HEIGHT CLASSIFICATION 66 5.1 Background 66 5.2 Classifier Design 68 5.2.1 Theory of Operation 69 5.2.2 Data Collection 73 5.2.3 Feature Selection 74 5.2.4 Training Process 81 5.3 Single Terrain Experiments 84 5.4 Mixed Terrain Experiments 87 5.4.1 Effects of Window Size 89 5.4.2 Testing Parameters 91 5.4.3 Flat Terrain Classifier 92 5.4.4 Mild Terrain Classifier 93 5.4.5 Rough Terrain Classifier 96 5.4.6 Voting Window 100 5.5 Conclusion 103 CHAPTER 6 SUMMARY AND FUTURE WORK 106 6.1 Summary 106 6.2 Future Work 106 LIST OF REFERENCES 108 APPENDIX A HARDWARE COMPONENT SPECIFICATIONS 117 ii LIST OF TABLES Table 2.1 HexaBull III specifications 12 Table 2.2 Environmental constants used in the RobotBuilder simulator 14 Table 4.1 Values for pitch and roll recorded when stepping up onto a 11.1 cm platform 60 Table 5.1 A summary of the terrain and robot parameters used for data collection 75 Table 5.2 Mean classification accuracy across multiple terrains per workspace 86 Table 5.3 Classification accuracies for several tests using the flat terrain classifier and different window sizes 93 Table 5.4 Classification accuracies for several tests using the mild terrain classifier and different window sizes 96 Table 5.5 Classification accuracies for several tests using the rough terrain classifier and different window sizes 100 Table A.1 Robotis Dynamixel MX-28T actuator specifications 117 Table A.2 Robotis OpenCM9.04 microcontroller specifications 118 iii LIST OF FIGURES Figure 2.1 The experimental hexapod HexaBull III 7 Figure 2.2 The states of the Force Threshold-based Position(FTP) controller during stance phase 8 Figure 2.3 A front view of HexaBull III 10 Figure 2.4 Redesigned leg of HexaBull III in the neutral state. 11 Figure 2.5 The simulated model of HexaBull III walking over an obstacle. 13 Figure 3.1 The foot coordinate frames for HexaBull III's six identical legs 18 Figure 3.2 The workspace volume for a single leg in its complete (a) and truncated (b) forms 19 Figure 3.3 The X-Z motion of the leg during a step. 21 Figure 3.4 The reachable points in the X-Z plane at a lateral distances of 10 cm (a) and 20 cm (b) from the hip 23 Figure 3.5 The Y-Z motion of the leg during a side step or turn 24 Figure 3.6 Two views of the approximated workspace after rectangle selection 25 Figure 3.7 A simplified workspace is created by fitting a parallelogram within the Y- Z plane of the approximated space (a) and then extruded in the fore-aft direction (b) 27 Figure 3.8 The cross-sectional workspace in the X-Y plane at the maximum height, zmax 28 Figure 3.9 The overhead view of one tripod and the associated planar workspaces at the start and end of a maneuver 29 Figure 3.10 The fore-aft position over the course of several steps during forward walk- ing 32 Figure 3.11 The foot motion of a right side step 35 iv Figure 3.12 Distance traveled by each workspace on flat terrain (a) and the random stepfield (b) 38 Figure 3.13 Total rotation in degrees by each workspace on flat terrain (a) and the random stepfield (b) 39 Figure 3.14 A comparison between a parallelogram and a more complex polygon shape 42 Figure 4.1 An example of a undetected ground contact (2a) when using an axial force sensing device (2b) at the foot 46 Figure 4.2 The measurement mechanism reacting to typical(4a) and early(4b) foot contacts with the terrain 48 Figure 4.3 The Ground Reaction Force (GRF) at the foot can be approximated from the measured rotation of the hip.