Layered LA-MAPF: a decomposition of large agent MAPF instance to accelerate solving without compromising solvability

TL;DR

This paper introduces Layered LA-MAPF, a decomposition approach that divides large-agent MAPF problems into clusters and levels, significantly reducing solving time and increasing success rates without sacrificing solution feasibility.

Contribution

The paper proposes a novel layered decomposition method for large-agent MAPF that accelerates solving by breaking down instances into manageable subproblems, enhancing existing algorithms.

Findings

Halves the average solving time from 40s to 20s.

Triples the success rate from 0.27 to 0.80 within 60 seconds.

Effectively scales with the number of agents across various maps.

Abstract

Multi-Agent Path Finding (MAPF) has been widely studied in recent years. However, most existing MAPF algorithms assume that an agent occupies only a single grid in a grid-based map. This assumption limits their applicability in many real-world domains where agents have geometric shapes, rather than being point-like. Such agents, which can occupy multiple cells simultaneously, are referred to as ``large'' agents. When considering the shape and size of agents in MAPF, the computational complexity increases significantly as the number of agents grows, primarily due to the increased overhead in conflict detection between geometric agents. In this paper, we propose two types of subproblems for the LA-MAPF (Large-Agent MAPF) problem: \textbf{cluster} (which has no constraints on the order of solution) and \textbf{level} (which imposes constraints on the solution order). We introduce \textbf{Layered LA-MAPF}, a method that decomposes a MAPF instance involving geometric agents into clusters, and then further decomposes each cluster into levels. This approach aims to reduce time complexity when solving LA-MAPF problems. Our results demonstrate the performance of our method as the number of agents increases across various maps, and how it accelerates LA-MAPF methods, such as LA-CBS and LA-LaCAM. Experiments show that our LA-MAPF method with instance decomposition \textbf{halves the time cost (reducing from an average of 40s to 20s) and triples the success rate (from an average of 0.27 to 0.80)} in finding a solution within 60 seconds. To facilitate further research, we have made the source code for Layered LA-MAPF publicly available at \url{https://github.com/JoeYao-bit/LayeredMAPF/algorithm/LA-MAPF}.