报告题目：Markov Chain-Based Stochastic Strategies for Robotic Surveillance
报告人：段晓明 博士，上海交通大学 助理教授
报告地点：3003必赢官网校本部升华后楼409
报告时间：2022年7月14日 10:30--
主持人：张永敏 3003必赢官网特聘教授
报告摘要：In this talk, we discuss the design of stochastic strategies for robotic surveillance tasks where a mobile robot moves on a graph to prevent potential intrusions. We will focus on two different approaches, i.e., an entropy maximization approach and a Stackelberg game-based approach. In the first approach, we study the novel problem of maximizing the return time entropy of a Markov chain, subject to a graph topology with travel times and stationary distribution. The return time entropy is the weighted average, over all graph nodes, of the entropy of the first return times of the Markov chain. The approach features theoretical and computational contributions. In the second approach, we consider the case where a potential intruder strategically attacks a location on the graph. The intruder is assumed to be omniscient: it knows the current location of the mobile agent and can learn the surveillance strategy. The goal for the mobile robot is to find a stochastic strategy so as to maximize the probability of capturing the intruder. We model the strategic interactions between the surveillance robot and the intruder as a Stackelberg game, and optimal and suboptimal surveillance strategies in star, complete and line graphs are studied.
讲者简介：Xiaoming Duan is an assistant professor in the Department of Automation at Shanghai Jiao Tong University (SJTU). Before joining SJTU, he was a postdoctoral fellow in the Oden Institute for Computational Engineering and Sciences at the University of Texas, Austin. He obtained his PhD degree in Mechanical Engineering from UC Santa Barbara in 2020, his Master's degree in Control Science and Engineering from Zhejiang University in 2016, and his Bachelor's degree in Automation from the Beijing Institute of Technology in 2013, respectively. His current research focuses on autonomous systems, multi-agent systems and robotics. He is a peer reviewer for IEEE TAC, Automatica, IEEE TCNS, etc.
