Transient Multi-Agent Path Finding for Lifelong Navigation in Dense Environments

TL;DR

This paper introduces Transient MAPF, a novel approach for lifelong multi-agent navigation that relaxes the simultaneous target-reaching constraint, leading to improved system throughput in dense environments.

Contribution

It proposes Transient MAPF as a new variant and algorithms for solving it, addressing limitations of traditional MAPF in lifelong navigation tasks.

Findings

Transient MAPF can improve throughput significantly.

Algorithms based on existing MAPF methods are effective for TMAPF.

Relaxing the simultaneous target-reaching constraint benefits dense environment navigation.

Abstract

Multi-Agent Path Finding (MAPF) deals with finding conflict-free paths for a set of agents from an initial configuration to a given target configuration. The Lifelong MAPF (LMAPF) problem is a well-studied online version of MAPF in which an agent receives a new target when it reaches its current target. The common approach for solving LMAPF is to treat it as a sequence of MAPF problems, periodically replanning from the agents' current configurations to their current targets. A significant drawback in this approach is that in MAPF the agents must reach a configuration in which all agents are at their targets simultaneously, which is needlessly restrictive for LMAPF. Techniques have been proposed to indirectly mitigate this drawback. We describe cases where these mitigation techniques fail. As an alternative, we propose to solve LMAPF problems by solving a sequence of modified MAPF problems, in which the objective is for each agent to eventually visit its target, but not necessarily for all agents to do so simultaneously. We refer to this MAPF variant as Transient MAPF (TMAPF) and propose several algorithms for solving it based on existing MAPF algorithms. A limited experimental evaluation identifies some cases where using a TMAPF algorithm instead of a MAPF algorithm with an LMAPF framework can improve the system throughput significantly.