Navigation and Physical Interaction with Balancing Robots

This work describes methods for advancing the state of the art in mobile robot navigation and physical Human-Robot Interaction (pHRI). An enabling technology in this effort is the ballbot, a person-sized mobile robot that balances on a ball. This underactuated robot presents unique challenges in planning, navigation, and control; however, it also has significant advantages over conventional mobile robots. The ballbot is omnidirectional and physically compliant. Moving requires the ballbot to lean, but this also gives it the ability to achieve both soft, compliant physical interaction and apply large forces. The work presented in this dissertation demonstrates the ability to navigate cluttered environments with the ballbot. Formulating the system as differentially flat enables fast, analytic trajectory planning. These trajectories are used to plan in the space of static and dynamic obstacles. Leveraging the ballbot’s navigational capabilities, this dissertation also presents a method of physically leading people by the hand. A human subject trial was conducted to assess the feasibility, safety, and comfort of this method. This study was successful, with the ballbot leading participants to multiple goals utilizing an amount of force that users found comfortable. Another area of pHRI explored in this dissertation is assisting people in transition from a seated position to standing. Another user study was conducted to discover how humans help each other out of chairs and how much force they apply. These data were used to design an impedance controller for the ballbot, and this controller was tested and found to deliver equivalent forces to those generated by people. Lastly, this work explores capabilities that could enable the ballbot to navigate through dense crowds of people. A method for detecting collision and estimating external forces was explored. This method was tested and used to modify a costmap. Iteratively updating this costmap and using it to plan trajectories enabled the robot to discover obstacles through collision. Because the ballbot is inherently compliant, these collisions resulted in safe interactions with small forces. Acknowledgments The work and effort required for a PhD is nearly impossible to achieve alone. My thesis is no exception, and I would like to thank those who have helped me in my journey. I would first like to thank all the people who make the Robotics Institute what it is. This place is truly special, and I am so thankful to have been a part of it. My labmates (MSLians) have been so critical to my work, helping me run experiments and discuss theories and methods. I would like thank Umashankar Nagarajan for all of his assistance in getting me started on the project and all of his work on the ballbot. My other labmates have helped run countless tests with ballbot and collaborated on so many ideas. Thank you Ankit Bhatia, Bhaskar Vaidya, Greg Seyfarth, Olaf-Dietrich Sanick, Chanapol Puapattanakun, and Tekin Mericli. I would also like to thank John Antanitis for his help conducting human subject trials. During my time in the lab, I’ve had the pleasure of working with and mentoring many summer students. This has been a fantastic teaching experience, but I have also learned so much from them. I would like to thank these students: Kenneth Payson, Carl Curren, Yufan (Adam) Zhang, Mark Dudley, Anson Wang, and Mike Lee. Although I did not collaborate with all the members of MSL, I did get to know them all. I am so fortunate to have had technical discussions, ask for advice, and become friends with these great people. Thank you Masaaki Kumagai, Camino Fernandez Llamas, Garth Zeglin, John Kozar, Teppei Tsujita, and Sebastian Schoendorfer. Outside of the lab, I have made so many fantastic friends in the Robotics Institute. There are far too many to mention, but please know that I am so thankful to have such great and supportive people in my life. My committee member, George Kantor, has given me advice and direction since I started working on the ballbot. I appreciate our many conversations about ideas and results. Thank you George. I would also like to thank Jodi Forlizzi, who has taught me a tremendous amount about creating positive interactions with robots and conducting subject trials. Thank you, also to my external committee member, Bill Smart. Our discussions about HRI and social navigation have been hugely helpful to me and my work. The person who made this work possible, my advisor Ralph Hollis deserves my deepest gratitude. It has been a pleasure to work with you, and I have learned so much from your mentorship. The way you approach robotics, fabrication, and design as well as your work ethic has influenced me more than you know. I would also like to thank Beth Hollis for brownies, banana bread, and generally being an amazing person. Thank you both for your incredible support and kindness. Lastly, I would like to thank my family. It has been a long road, and I wouldn’t have made it here with your support. I especially need to thank my wife, Liz. Being PhD students and living in different cities has certainly not been easy, but I couldn’t have done it without you. Thank you for the years of support and motivation.