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

TL;DR

This work addresses the mismatch between MAPF and Lifelong MAPF (LMAPF) by introducing Transient MAPF (TMAPF), a variant where each agent must visit its target without requiring all agents to finish simultaneously. The authors adapt popular MAPF solvers (PrP, LNS, CBS) to TMAPF by extending the search to track whether an agent has visited its target, enabling solutions in scenarios where traditional MAPF planning fails. Experimental evaluation within a rolling-horizon framework shows that TMAPF can significantly improve throughput in dense environments when the potential targets are limited to a small set, though gains are modest in uniformly distributed target settings. The work demonstrates a direct route to mitigating the MAPF-LMAPF mismatch and suggests future directions around optimizing end positions and extending TMAPF to tasks that require time-at-target to complete.

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.

Figures (3)

• Figure 1: LMAPF problem that cannot be solved as a sequence of MAPF problems.
• Figure 2: An LMAPF problem in which pathological behaviors occur under different conditions and solvers.
• Figure 3: Throughput achieved by the different algorithms as factor of the number of agents.