Routing for Persistent Exploration in Dynamic Environments with Teams of Energy-Constrained Robots Derek Mitchell

Routing for Persistent Exploration in Dynamic Environments with Teams of Energy-Constrained Robots Derek Mitchell Sept 14, 2020 CMU-RI-TR-20-52 The Robotics Institute School of Computer Science Carnegie Mellon University Pittsburgh, PA Thesis Committee: Nathan Michael, CMU, Chair Katia Sycara, CMU Maxim Likhachev, CMU Stephen L. Smith, University of Waterloo Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Robotics. Copyright c 2020 Derek Mitchell Abstract Disaster relief scenarios require rapid and persistent situational awareness to in- form first-responders of safe and viable routes through a constantly shifting environment. Knowing what roads have become flooded or are suddenly obstructed by debris can significantly improve response time and ease the distribution of resources. In a sufficiently large environment, deploying and maintaining fixed camera stands would be ineffective and prohibitively expensive, so we look to deploying teams of robots equipped with sensors to persistently cover the region of interest. In this case, the main challenge is determining how to distribute the robots to cover the entire region with limited travel speed and duration. Basic multi-robot coverage and exploration methods take a passive approach that direct robots to evenly cover the space and populate a map of the environment with the observations the robots acquire as they move. More reactive frontier-based approaches will continuously guide robots towards unobserved regions until the environment is fully known. These approaches, however, are less effective when the environment changes over time. When the number of robots is limited and can only operate for finite durations, the planner must prioritize which regions to visit in order to provide the most accurate map possible. In this thesis, we propose to plan deployments of teams of quadrotors equipped with range sensors to cooperatively cover an environment such that a map can be persistently updated as the environment topography evolves. Here, we present a systems-based approach that breaks up planning into stages and computes feasible plans over a sliding-window horizon. These plans are extended over subsequent horizons up to the limit that each robot’s battery capacity will allow, ending with robots returning to home base and recharging. We initially show that the proposed planner is able to outperform greedy frontier assignment in terms of map accuracy and confidence. We then show how the objective function we initially used to distribute robots can be modified to incorporate the ‘goodness-of-fit’ of the environment dynamics model by biasing robots towards regions whose dynamics are less under- stood. The updated objective results in a system that quickly converges to robots revisiting regions only as often as they are expected to change. While there is a physical limitation to how much area a team of energy-constrained robots can cover persistently, the system we present leverages an understanding of environment dynamics to maximize model improvement over time in a computa- tionally tractable fashion. This is shown with a comprehensive study of the computational complexity of each component of the proposed system and an evaluation of how overall performance evolves over time relative to the choice of system parameters and environment conditions. We additionally show that the system can be tuned to better address environments experiencing changes of greatly varying magnitudes and scale. The result is a complete system that enables perpetual deployment and efficient distribution of robots throughout the environment to ensure no changes go unobserved for too long. Acknowledgments I can confidently say I could not have come this far without the support of my mentors over the years. I have been extremely fortunate to have constant access to teachers, supervisors, colleagues, and advisors that see potential in me and provide ample support to help draw it out. In particular I am most grateful to my advisor, Nathan Michael, who not only provided the opportunities, resources, and advice I needed to polish my skills and delve deeply into engaging research, but also gave me many chances to put my efforts into public view. His constant drive to push the boundaries of the state of the art in a manner that is visible and accessible to those it may interest has had significant impact in shaping my approach to research. I would also like to thank my thesis committee members, Katia Sycara, Maxim Likhachev, and Stephen Smith, who have been very helpful and accommodating throughout this process, with special thanks to Katia who helped co-advise me during my time as a Masters student. Next, I would like to thank all my friends and colleagues at the Resilient Intelli- gent Systems Lab, whose care and advice has made this long journey enjoyable and rewarding. Foremost, I would like to thank my office mates, Ellen Cappo, Micah Corah, and the fish for their constant care, advice, and company (especially the fish). I have also received ample research advice and help managing robots from Arjav Desai, Matt Collins and Curtis Boirum, without whom much of my more interesting demonstrations would never have come to fruition. Many others in the lab, past and present, have helped me to polish my research and presentations through discussion and peer review, sacrificing their time and attention to help better my work. In particular, I offer my gratitude to Wennie Tabib, Kumar Shaurya Shankar, John Yao, Xun- ing Yang, Cormac O’Meadhra, Aditya Dhawale, Alex Spitzer, Lauren Lieu, Mosam Dabhi, Vishnu R. Desaraju, Vibhav Ganesh, Erik Nelson and Shihyun Lo. Outside of the lab, I want to give special thanks Allie Del Giorno, whose friend- ship over the last seven years has helped me maintain some level of sanity and kept me from disappearing into the solitude of my apartment. I also have to offer my gratitude to Chuck Whittaker and James Teza, who worked directly with me to build many of the complex charging interfaces I needed to test my long duration deployments, as well as Karen Widmaier, Ashley McClinton, Nora Kazour, and Suzanne Lyons Muth who have provided ample administrative support to help me navigate the Ph.D. process smoothly. Finally, I would like to thank my friends and family back home, who have been extremely patient and allowed me to disappear into my work for the past half a decade or so. While I appreciate the constant support and encouragement, the great- est help has been their forgiveness for missing the occasional holiday. Knowing that I can pour all my effort into my work when deadlines loom overhead, certain in the knowledge that my family is supporting me from home and will be there when I need them, has given me the strength and freedom to tackle challenges however suits me best. Contents 1 Introduction 1 1.1 Core Challenges . .4 1.2 Summary of Contributions . .5 2 Background 7 2.1 Environment Mapping . .9 2.1.1 Dynamic Environments . 11 2.1.2 Active Perception . 12 2.2 Motion Planning . 13 2.3 Long-Duration Autonomy . 16 2.3.1 Capacitated Routing . 17 3 Persistent Multi-Robot Mapping in an Uncertain Environment 23 3.1 Problem Formulation . 23 3.2 Methodology . 25 3.3 Simulation Evaluation . 38 3.4 Conclusion . 43 4 Allocating Limited Sensing Resources to Accurately Map Dynamic Environments 44 4.1 Problem Definition . 44 4.2 Methodology . 47 4.2.1 HMM Occupancy Grid . 47 4.2.2 Observation Utility Measure . 49 4.3 Results . 51 4.3.1 Experiment Setup . 51 4.3.2 Comparison of Objective Functions . 52 4.3.3 Varying Parameters . 55 4.3.4 Varying Environments . 57 4.4 Discussion . 58 4.5 Conclusion . 58 5 Deployment Planning for Online Mapping of Dynamic Environments 59 5.1 Environment Model . 62 5.1.1 Computational Complexity . 63 iv 5.2 Sensor Model . 63 5.2.1 Computational Complexity . 65 5.2.2 Observation Utility . 65 5.2.3 Computational Complexity - Utility Function . 67 5.3 Waypoint Selection . 68 5.3.1 Computational Complexity . 70 5.4 Path Planning . 71 5.4.1 Trajectory Generation . 72 5.4.2 Computational Complexity . 73 5.5 Waypoint Assignment . 74 5.5.1 Greedy Assignment . 74 5.5.2 Minimum Cost VRP . 76 5.5.3 Min-Makespan VRP . 77 5.5.4 Ensuring Feasible Configurations . 78 5.5.5 Computational Complexity . 80 5.6 Conclusion . 81 6 Experimental Evaluation in Simulated Environments 82 6.1 Setup Details . 83 6.2 Approach Comparison . 85 6.3 Parameter Evaluation . 91 6.4 Physical Limits . 99 6.5 Environment Conditions . 106 6.6 Urban Environment . 110 6.7 Conclusion . 113 7 Conclusion 117 7.1 Summary of Contributions . 118 7.2 Future Work . 119 A Online Energy-Constrained Adaptation and Scheduling of Persistent Coordinated Behavior-Based Multi-Robot Deployments 123 A.1 System Overview . 124 A.2 Experimental Evaluation . 136 A.3 Conclusion . 141 Bibliography 142 v List of Figures 1.1 Images showing the devastation that can be caused by natural disaster. .2 2.1 Visualization of the intersection of related fields to which the proposed work belongs. .8 2.2 Three distinct models of environment topography. .9 2.3 Enforcing long duration operation trajectory swapping. 17 2.4 Graphical representation of a Vehicle Routing Problem. 20 3.1 Deployment planner system diagram . 24 3.2 The planning pipeline expressed in graph form . 26 3.3 The observation model . 27 3.4 Confidence decay for the log-odds and probability representation of occupancy likelihood . 29 3.5 Waypoint Selection uses the Sensor Model to determine the optimal observation locations . 30 3.6 Representation of the simulated environment populated by static and dynamic objects .

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