MAPF-HD: Multi-Agent Path Finding in High-Density Environments

TL;DR

The phased null-agent swapping (PHANS) method is introduced, a heuristic approach to incrementally swap positions between agents and empty vertices that solves the MAPF-HD problem within a few seconds, even in large environments containing more than $700$ cells.

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, it becomes necessary to 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 take tens to hundreds of seconds. Such 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 a few seconds, even in large environments containing more than $700$ cells. The proposed method has the potential to improve efficiency in various real-world applications such as warehouse logistics, traffic management, and crowd control. The implementation is available at https://github.com/ToyotaCRDL/MAPF-in-High-Density-Envs.

Figures (13)

• Figure 1: MAPF in a high-density warehouse environment. Only a subset of target agents (those with matching-colored flags) are assigned goals, whereas the rest act as movable obstacles. In addition to planning paths for target agents to reach their goals, efficiently relocating obstructing agents is crucial for successful navigation under high-density conditions.
• Figure 2: Simulation examples of MAPF in (a) low-density and (b) high-density environments. Colored lines represent the paths of the target agents. Unlike low-density environments, obstructing agents are present along these paths in high-density environments. The target agents are moved while the obstructing agents are appropriately evacuated.
• Figure 3: Example of the MAPF-HD problem: (a) initial positions, (b) evacuation of obstructing agents, and (c) movement of the target agent.
• Figure 4: Second stage of PHANS: Null-agent swapping mechanism.
• Figure 5: Simulation environment for Experiment 1. The grid size is $14\times 7$. Obstructing agents occupy $90\%$ of the cells.