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Abstract
Formation control of multi-robot systems has been extensively studied by model-based methods, where analytic control inputs are constructed based on the kinematics and/or dynamics model and the communication graphs of the multi-robot system. Recently, driven by remarkable advances of robotic learning techniques, emerging studies on learning-based methods for formation control have been developed for adaptive and intelligent control of multi-robot systems. This paper aims to provide a brief overview of our recent development of learning-based formation control, and compare it with a model-based method for a case study of three-robot formation control. Fundamental principles, experimental results and technical challenges are presented, comparing the two different methodologies.
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No potential conflict of interest was reported by the authors.
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This article has been republished with minor changes. These changes do not impact the academic content of the article.
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Chao Jiang
Chao Jiang received the B.S. degree in measuring and control technology and instrument from Chongqing University, Chongqing, China, in 2009, and the Ph.D. degree in electrical engineering from Stevens Institute of Technology, Hoboken, NJ, USA, in 2019. He worked as a research assistant in the Key Laboratory of Optoelectronic Technology and Systems (Chongqing University), Ministry of Education, China, from 2009 to 2012. He joined the Department of Electrical and Computer Engineering at the University of Wyoming, Laramie, WY, USA, in Fall 2019, where he is currently an Assistant Professor. His current research interests include autonomous robots, human–robot interaction, robotic learning, deep reinforcement learning, and multi-robot systems. He is the recipient of the Innovation & Entrepreneurship Doctoral Fellowship (2012–2016), and the Outstanding Ph.D. Dissertation Award in Electrical Engineering (2019) at Stevens Institute of Technology.
Zhuo Chen
Zhuo Chen received his B.E. in automation from Zhengzhou University, Zhengzhou, China, in 2013, and M.S. in Control Science and Engineering from Harbin Institute of Technology, Harbin, China, in 2015. He is a Ph.D. candidate in electrical engineering at Stevens Institute of Technology, Hoboken, USA. His main research interests include autonomous mobile robotics and human–robot interaction.
Yi Guo
Yi Guo received her B.S. and M.S. from Xi'an University of Technology, Xi'an, China, in 1992 and 1995, respectively, and Ph.D. from the University of Sydney, Australia, in 1999, all in electrical engineering. She was a Postdoctoral Research Fellow at Oak Ridge National Laboratory from 2000 to 2002, and a Visiting Assistant Professor at the University of Central Florida, Orlando, FL, USA, from 2002 to 2005. She joined the Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA, in 2005, where she is currently a Professor. Her main research interests include autonomous mobile robotics, distributed sensor networks, and nonlinear control systems. She has published more than 100 peer-reviewed journal and conference papers, authored the book entitled “Distributed Cooperative Control: Emerging Applications” (John Wiley & Sons 2017), and edited a book on micro/nano-robotics for biomedical applications (Springer 2013). She currently serves on the editorial boards of several journals including IEEE Robotics and Automation Magazine and IEEE/ASME Transactions on Mechatronics. She served in Organizing Committees of IEEE International Conference on Robotics and Automation (2015, 2014, 2008, 2006).