Multi Agent Path Planning

Multi-agent path planning (MAPP) focuses on efficiently and safely coordinating the movement of multiple agents, such as robots or vehicles, to reach their destinations without collisions. Current research emphasizes developing algorithms that handle large numbers of agents, incorporate real-world constraints like limited sensor information and communication ranges, and optimize for metrics like makespan and throughput. Prominent approaches include reinforcement learning, particularly with cooperative reward shaping, and methods based on network flow optimization and timed roadmaps. These advancements have significant implications for various applications, including autonomous vehicle navigation, robotic systems, and air traffic management.