Probabilistic pursuit-evasion games: theory, implementation, and experimental evaluation

TLDR

The study proposes a distributed hierarchical hybrid system for UAVs and UGVs to pursue evaders while simultaneously mapping an unknown environment. The authors model the pursuit‑evasion task as a probabilistic game and implement two greedy policies, local‑mar and global‑max, within a distributed architecture that handles high‑level policy computation, mapping, communication, and low‑level navigation. Simulation and field experiments show that the proposed policies’ capture times depend on evader speed, intelligence, and pursuer sensing, demonstrating the effectiveness of the distributed system.

Abstract

We consider the problem of having a team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) pursue a second team of evaders while concurrently building a map in an unknown environment. We cast the problem in a probabilistic game theoretical framework, and consider two computationally feasible greedy pursuit policies: local-mar and global-max. To implement this scenario on real UAVs and UGVs, we propose a distributed hierarchical hybrid system architecture which emphasizes the autonomy of each agent, yet allows for coordinated team efforts. We describe the implementation of the architecture on a fleet of UAVs and UGVs, detailing components such as high-level pursuit policy computation, map building and interagent communication, and low-level navigation, sensing, and control. We present both simulation and experimental results of real pursuit-evasion games involving our fleet of UAVs and UGVs, and evaluate the pursuit policies relating expected capture times to the speed and intelligence of the evaders and the sensing capabilities of the pursuers.