Navigation and Physical Interaction with Balancing Robots
Navigation and Physical Interaction with Balancing Robots
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平衡机器人的导航和物理交互
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Michael Shomin
中科院分区：
文献类型：
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作者：
Michael Shomin
这项工作描述了用于推进最先进的移动的机器人导航和物理人机交互（pHRI）的方法。这项工作中的一项使能技术是球形机器人，这是一种在球上保持平衡的人大小的移动的机器人。这种欠驱动机器人提出了独特的挑战，规划，导航和控制，但是，它也有显着的优势，传统的移动的机器人。球形机器人是全方位的，物理上是兼容的。移动需要球机器人倾斜，但这也使它能够实现柔软，顺应性的物理交互和施加大的力。本论文的工作展示了球形机器人在复杂环境中的导航能力。将系统公式化为差分平坦可以实现快速的分析轨迹规划。这些轨迹用于在静态和动态障碍物的空间中进行规划。利用球形机器人的导航能力，本文还提出了一种方法，物理领导人的手。进行了人类受试者试验以评估该方法的可行性、安全性和舒适性。这项研究是成功的，球形机器人利用用户感到舒适的力量带领参与者实现多个目标。在这篇论文中探讨的另一个领域的pHRI是协助人们从坐姿到站立的过渡。另一项用户研究是为了发现人们如何从椅子上互相帮助，以及他们施加了多大的力量。这些数据被用来设计一个球机器人的阻抗控制器，这个控制器进行了测试，发现提供等效的力量，由人产生的。最后，这项工作探索了能够使球形机器人在密集人群中导航的能力。探索了一种碰撞检测和外力估计的方法。对该方法进行了测试，并用于修改成本图。迭代更新这个成本图并使用它来规划轨迹，使机器人能够通过碰撞发现障碍物。由于球形机器人本身是顺应性的，这些碰撞导致了小力量的安全相互作用。致谢博士学位所需的工作和努力几乎是不可能单独实现的。我的论文也不例外，我要感谢那些在我的旅程中帮助过我的人。首先，我要感谢所有使机器人研究所成为今天这个样子的人。这个地方真的很特别，我很感激能成为其中的一部分。我的实验室伙伴（MSLians）对我的工作非常重要，帮助我进行实验并讨论理论和方法。我要感谢Umashankar Nagarajan在我开始这个项目时提供的所有帮助，以及他在球机器人上的所有工作。我的其他实验室同事帮助我进行了无数的测试，并在很多想法上进行了合作。感谢Ankit Bhatia、Bhaskar Vaidya、Greg Seyfarth、Olaf-Dietrich Sanick、Chanapol Puapattanakun和Tekin Mericli。我还要感谢John Antanitis，他帮助进行了人体试验。在实验室工作期间，我有幸与许多暑期学生一起工作并指导他们。这是一次很棒的教学经历，但我也从中学到了很多。我要感谢这些学生：肯尼斯·佩森、卡尔·柯伦、张宇凡（亚当）、马克·达德利、安森·王和迈克·李。虽然我没有与MSL的所有成员合作，但我确实认识了他们。我很幸运能与这些伟大的人进行技术讨论，寻求建议，并成为朋友。感谢Masaaki Kumagai、Camino费尔南德斯Llamas、Garth Zeglin、John Kozar、Teppei Tsujita和塞巴斯蒂安Schoendorfer。在实验室之外，我在机器人研究所交了很多很棒的朋友。有太多太多的提到，但请知道，我是如此感谢有这样的伟大和支持的人在我的生活。我的委员会成员，乔治坎特，给了我的建议和方向，因为我开始工作的球机器人。我很欣赏我们关于想法和结果的许多对话。谢谢你乔治。我还要感谢Jodi Forlizzi，他教会了我很多关于与机器人建立积极互动和进行主题试验的知识。谢谢你，也谢谢我的外部委员会成员，比尔·斯马特。我们关于HRI和社交导航的讨论对我和我的工作有很大的帮助。使这项工作成为可能的人，我的顾问拉尔夫·霍利斯值得我最深切的感谢。很高兴与你共事，我从你的指导中学到了很多。你处理机器人的方式，制造，设计以及你的职业道德对我的影响比你知道的要大。我还要感谢贝丝·霍利斯的布朗尼，香蕉面包，总的来说，她是一个了不起的人。感谢你们的支持和善意。最后，我要感谢我的家人。这是一条漫长的道路，如果没有你们的支持，我不会走到今天。我特别要感谢我的妻子莉兹。作为博士生，生活在不同的城市肯定不容易，但没有你，我不可能做到。感谢您多年来的支持和鼓励。
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.