Pursuit Evasion

Pursuit-evasion research focuses on modeling and solving the problem of one or more pursuers attempting to capture an evader, often within complex environments. Current research emphasizes the development of robust algorithms, including deep reinforcement learning (especially multi-agent variants), particle swarm optimization, and graph neural networks, to optimize pursuer strategies and account for factors like partial observability, dynamic environments, and heterogeneous agent capabilities. These advancements have implications for diverse applications such as autonomous navigation, surveillance, search and rescue, and even military applications involving unmanned aerial and surface vehicles. The ultimate goal is to develop efficient and reliable algorithms for achieving capture or interception in a variety of challenging scenarios.