A hierarchical approach to probabilistic pursuit-evasion games with unmanned ground and aerial vehicles

Abstract

We consider the problem of having a team of unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV) pursue a team of evaders while concurrently building a map in an unknown environment. We cast this problem in a probabilistic game-theoretic framework and consider two computationally feasible pursuit policies: greedy and global-max. We implement this scenario on a fleet of UGVs and UAVs by using a distributed hierarchical system architecture. Finally, we present both simulation and experimental results that evaluate the pursuit policies relating expected capture times to the speed and intelligence of the evaders and the sensing capabilities of the pursuers.