Cooperative Pursuit

Cooperative pursuit research focuses on developing algorithms enabling multiple agents to collaboratively track and capture a target, addressing challenges like decentralized coordination and limited sensor information in complex environments. Current work emphasizes hybrid approaches combining reinforcement learning with methods like artificial potential fields and attention mechanisms, or leveraging game-theoretic models and distributed estimation techniques such as Kalman filtering and recursive least squares. These advancements improve the efficiency, robustness, and adaptability of cooperative pursuit strategies, with applications ranging from autonomous aerial surveillance to multi-robot search and rescue operations.