Consider a scenario in which a surveillance Uninhabited Aerial Vehicle (UAV) with a limited sensing radius detects a target – an adversarial vehicle trying to escape a protected area. Simultaneously, an interceptor vehicle is tasked with reaching the mobile target. However, the interceptor relies on the UAV to provide it with the target’s instantaneous location and velocity. Then, how should the UAV move to maximize the time it can keep the target within its sensing range for a successful interception? Can the target outrun the UAV and escape interception? In short, how does the interplay between sensing constraints and vehicle kinematics in this mutual support scenario impact the outcome of this game?
In this talk, I will present a mathematical formulation of the mutual support scenario discussed above in the form of a pursuit-evasion game. It is played between a sensor-attacker team and a mobile target in the unbounded Euclidean plane. The target is faster than the sensor, but slower than the attacker. The sensor’s objective is to keep the target within a sensing radius so that the attacker can capture the target, whereas the target seeks to escape by reaching beyond the sensing radius from the sensor without getting captured. We assume that as long as the target is within the sensing radius from the sensor, the sensor-attacker team is able to measure the target’s instantaneous position and velocity. We will pose and solve this problem as a game of kind and present a novel analysis that characterizes target speeds below which capture can be guaranteed and above which there is a guaranteed escape strategy for the target. I will then discuss recent extensions of these results to maneuvering targets and limited communication between the sensor and the attacker.
Shaunak D. Bopardikar is an Associate Professor with the Electrical and Computer Engineering Department at Michigan State University (MSU). He received his Ph.D. in Mechanical Engineering from the University of California Santa Barbara (UCSB) in 2010 under the joint supervision of Prof. Francesco Bullo and Prof. Joao P. Hespanha. Subsequently, he worked as a post-doctoral associate at UCSB with Prof. Hespanha until 2011. He then joined the Controls group of United Technologies Research Center (UTRC) at Berkeley, CA and then at East Hartford, CT, USA as a Senior/Staff Research Scientist before joining MSU in 2018. His research and teaching interests include game theory, control and randomized algorithms. His recognitions include a 2023 National Science Foundation CAREER award, the 2023 IEEE Technical Committee on Security and Privacy’s Best Student Paper award (as advisor) and a 2024 MSU College of Engineering’s Withrow Excellence in Teaching Award. He is a senior member of the IEEE.
