Multi-Agent Path Finding Based on Subdimensional Expansion with Bypass

TL;DR

This paper introduces BPM*, a novel MAPF algorithm that incorporates bypass into subdimensional expansion to reduce redundant computation and improve efficiency in solving benchmark problems.

Contribution

It presents BPM*, a new MAPF algorithm that enhances subdimensional expansion with bypass to optimize pathfinding performance.

Findings

BPM* outperforms existing algorithms on benchmark MAPF problems.

The bypass technique reduces redundant collision set updates.

BPM* demonstrates improved efficiency and solution quality.

Abstract

Multi-agent path finding (MAPF) is an active area in artificial intelligence, which has many real-world applications such as warehouse management, traffic control, robotics, etc. Recently, M* and its variants have greatly improved the ability to solve the MAPF problem. Although subdimensional expansion used in those approaches significantly decreases the dimensionality of the joint search space and reduces the branching factor, they do not make full use of the possible non-uniqueness of the optimal path of each agent. As a result, the updating of the collision sets may bring a large number of redundant computation. In this paper, the idea of bypass is introduced into subdimensional expansion to reduce the redundant computation. Specifically, we propose the BPM* algorithm, which is an implementation of subdimensional expansion with bypass in M*. In the experiments, we show that BPM* outperforms the state-of-the-art in solving several MAPF benchmark problems.