Multi Agent Path Finding

Multi-agent pathfinding (MAPF) focuses on efficiently finding collision-free paths for multiple agents navigating a shared environment, aiming to minimize travel time and resource consumption. Current research emphasizes improving the scalability and robustness of algorithms like Conflict-Based Search (CBS) and its variants, exploring machine learning approaches (including imitation learning and reinforcement learning) to create decentralized and adaptable solutions, and developing more realistic models incorporating continuous environments and agent dynamics. These advancements are crucial for optimizing various applications, including warehouse automation, traffic management, and multi-robot coordination, where efficient and safe navigation of multiple agents is paramount.