📝 SummarXiv

Abstract

Multi-agent path finding (MAPF) involves planning efficient paths for multiple agents to move simultaneously while avoiding collisions. In typical warehouse environments, agents are often sparsely distributed along aisles. However, increasing the agent density can improve space efficiency. When the agent density is high, we must optimize the paths not only for goal-assigned agents but also for those obstructing them. This study proposes a novel MAPF framework for high-density environments (MAPF-HD). Several studies have explored MAPF in similar settings using integer linear programming (ILP). However, ILP-based methods require substantial computation time to optimize all agent paths simultaneously. Even in small grid-based environments with fewer than $100$ cells, these computations can incur tens to hundreds of seconds. These high computational costs render these methods impractical for large-scale applications such as automated warehouses and valet parking. To address these limitations, we introduce the phased null-agent swapping (PHANS) method. PHANS employs a heuristic approach to incrementally swap positions between agents and empty vertices. This method solves the MAPF-HD problem within seconds to tens of seconds, even in large environments containing more than $700$ cells. The proposed method can potentially improve efficiency in various real-world applications such as warehouse logistics, traffic management, or crowd control. Code is available at https://github.com/ToyotaCRDL/MAPF-in-High-Density-Envs.