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Robot formation movement inspired by animal groups





Ming Cao is the director of the Jantina Tammes School of Digital Society, Technology and AI, and a professor of systems and control at the University of Groningen, the Netherlands. He received the Bachelor degree in 1999 and the Master degree in 2002 from Tsinghua University, China, and the Ph.D. degree in 2007 from Yale University, USA, all in Electrical Engineering. From 2007 to 2008, he was a Research Associate at Princeton University, USA. He worked as a research intern in 2006 at the IBM T. J. Watson Research Center, USA. He is the 2017 and inaugural recipient of the Manfred Thoma medal from the International Federation of Automatic Control (IFAC) and the 2016 recipient of the European Control Award sponsored by the European Control Association (EUCA). He is an IEEE fellow. He is a Senior Editor for Systems and Control Letters, an Associate Editor for IEEE Transactions on Automatic Control, IEEE Transactions on Control of Network Systems and IEEE Robotics and Automation Magazine, and was an associate editor for IEEE Transactions on Circuits and Systems and IEEE Circuits and Systems Magazine. He is a member of the IFAC Conference Board and a vice chair of the IFAC Technical Committee on Large-Scale Complex Systems. His research interests include autonomous agents and multi-agent systems, complex networks and decision-making processes.


Animal formation movements, from bird flocks to fish schools, are enabled by intriguing mechnimsms that utilize local sensing signals to realize global collective motion coordination. The design of formation control strategies for teams of mobile robots can benifit from the better understanding of how animals implement sensing or communication topologies within groups. Along this line of research, rigidity graph theory turns out to be a powerful tool to gain insight into how multi-agent structures become rigid under inter-agent distance or angle constraints. In this talk, I will report recent developments on how such rigidity properties of multi-agent formations may lead to different local and global behaviors when the available types of inter-agent sensing signals change. In particular, I emphasize the importance of formation robustness against sensing noises and correspondingly the effectiveness of estimator-based formation control.

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