《Controlling formations of mobile robots under inconsistent measurements》
《Towards Optimal Control of Evolutionary Games on Networks》
Title: Controlling formations of mobile robots under inconsistent measurements
Abstract: Teams of autonomous robots that work cooperatively are used more and more widely for a range of robotic tasks such as environmental monitoring and coordinated transportation. Robots have been deployed in formations with different shapes in order to facilitate the adaptive sampling of an unknown environment or to achieve better cooperation efficiency. As a result, considerable research efforts have been made in the past few years on designing distributed control laws to stabilize the shapes of formations of autonomous robots. In particular, within the research area of developing cooperative control theory for multi-agent systems, a sequence of theoretical investigations have been made to design formation control laws using the notion of graph rigidity, and such control laws are usually based on the gradients of the potential functions. However, it has been recently reported that for such gradient control laws, if agents disagree with their neighboring peers on the measured or prescribed distances between them, undesirable formation motion might appear. In this talk, I will demonstrate how local estimators can be exploited to stabilize robotic formations even under inconsistent measurements.
Title: Towards Optimal Control of Evolutionary Games on Networks
Abstract: We investigate the control of evolutionary games on networks, in which each edge represents a two-player repeated game between neighboring agents. After each round of games, agents can update their strategies based on local payoff and strategy information, while a subset of agents can be assigned strategies and thus serve as control inputs. We seek here the smallest set of control agents needed to drive the network to a desired strategy state. After presenting an exact solution that is practical only for small networks due to its computational complexity, we design a fast algorithm for approximating the solution to a simplified problem on tree networks. We then show how to extend this approach to the general problem on arbitrary networks using a weighted minimum spanning tree and strategy propagation algorithm. We show that the resulting approximation is exact for certain classes of games on complete, ring, and star networks. Finally, simulations demonstrate that the algorithm yields near-optimal solutions for a wide range of cases.
Ming Cao is currently chair professor of networks and robotics with the Engineering and Technology Institute (ENTEG) at the University of Groningen, the Netherlands, where he started as a tenure-track assistant professor in 2008. He received the Bachelor degree in 1999 and the Master degree in 2002 from Tsinghua University, Beijing, China, and the PhD degree in 2007 from Yale University, New Haven, CT, USA, all in electrical engineering. From September 2007 to August 2008, he was a postdoctoral research associate with the Department of Mechanical and Aerospace Engineering at Princeton University, Princeton, NJ, USA. He worked as a research intern during the summer of 2006 with the Mathematical Sciences Department at the IBM T. J. Watson Research Center, NY, USA. He is the 2017 and inaugural recipient of the Manfred Thoma Medal from the International Federation of Automatic Control (IFAC) for his outstanding contribution to the field of systems and control as a young researcher under 40 years old. He is the 2016 recipient of the European Control Award sponsored by the European Control Association (EUCA) for his fundamental contributions to distributed and cooperative control of multi-agent systems and complex networks. He is an associate editor for IEEE Transactions on Automatic Control, IEEE Transactions on Circuits and Systems and Systems and Control Letters. He is also a member of the IFAC Technical Committee on Networked Systems. His main research interest is in autonomous agents and multi-agent systems, mobile sensor networks and complex networks.