Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics that asks us to compute collision-free paths for a team of agents, all moving across a shared map. Although many works appear on this topic, all current algorithms struggle as the number of agents grows. The principal reason is that existing approaches typically plan free-flow optimal paths, which creates congestion. To tackle this issue we propose a new approach for MAPF where agents are guided to their destination by following congestion-avoiding paths. We evaluate the idea in two large-scale settings: one-shot MAPF, where each agent has a single destination, and lifelong MAPF, where agents are continuously assigned new tasks. For one-shot MAPF we show that our approach substantially improves solution quality. For Lifelong MAPF we report large improvements in overall throughput.

翻译：多智能体路径规划（MAPF）是机器人学中的一个基础问题，要求我们为一组在共享地图上移动的智能体计算无碰撞路径。尽管该领域已涌现大量研究，但现有算法在面对智能体数量增长时均表现受限。根本原因在于现有方法通常规划自由流动的最优路径，这会导致拥堵问题。为此，我们提出了一种新的MAPF方法，通过引导智能体沿避堵路径抵达目标位置。我们在两种大规模场景下评估该思路：一次性MAPF（每个智能体有单一目的地）和终身MAPF（智能体持续接收新任务分配）。实验表明，在一次性MAPF中，我们的方法显著提升了解决方案质量；在终身MAPF中，我们的方法实现了整体吞吐量的大幅提升。