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）是机器人领域中的一个基本问题，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。尽管该领域已有大量研究，但现有算法在智能体数量增加时均面临挑战。主要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。为解决这一问题，我们提出了一种新的MAPF方法，通过引导智能体沿避拥堵路径前往目的地。我们在两种大规模场景中评估了该思路：一次性MAPF（每个智能体只有一个目的地）和终身MAPF（智能体持续被分配新任务）。对于一次性MAPF，我们证明该方法显著提升了求解质量。对于终身MAPF，我们报告了整体吞吐量的大幅提升。