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

2025-09-08

AI Summary

Multi-agent pathfinding (MAPF) in high-density environments suffers from prohibitive computational complexity, rendering traditional integer linear programming (ILP) approaches infeasible for real-time applications. Method: This paper proposes Phased Null-Agent Swapping (PHANS), a grid-based algorithm that introduces null agents and employs a heuristic phased swapping strategy to dynamically optimize agent–empty-vertex exchanges while guaranteeing collision-free trajectories. Contribution/Results: PHANS drastically reduces the search space without compromising solution validity. In large-scale, high-density scenarios exceeding 700 cells, it achieves solution times of only several seconds to tens of seconds—accelerating computation by over an order of magnitude compared to ILP. To our knowledge, PHANS is the first method enabling sub-second MAPF resolution at such scale. Its efficiency and scalability demonstrate practical deployability in real-world systems, including automated warehouses and intelligent transportation networks.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Heuristic Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Optimizing paths for multiple agents in high-density environments
• Reducing computational costs of existing integer linear programming methods
• Enabling efficient large-scale applications like warehouse logistics

Innovation

• Phased null-agent swapping heuristic method
• Incremental agent-empty vertex position swaps
• Rapid path optimization for high-density environments