Lifelong Multi Agent Path Finding

Lifelong Multi-Agent Path Finding (LMAPF) addresses the challenge of efficiently coordinating multiple agents navigating a shared environment with continuously arriving tasks. Current research focuses on improving algorithm efficiency and scalability for large numbers of agents and high-density scenarios, often employing techniques like caching, guidance graphs, and hybrid planning-learning approaches to mitigate congestion and enhance throughput. These advancements are crucial for optimizing real-world applications such as warehouse automation and robotics, where efficient task allocation and collision avoidance are paramount. The field is actively exploring decentralized solutions and more realistic models to bridge the gap between theoretical LMAPF and practical deployment.