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.

翻译：多智能体路径规划（MAPF）旨在为一组智能体寻找从初始配置到给定目标配置的无冲突路径。终身MAPF（LMAPF）是MAPF的一个被广泛研究的在线版本，其中智能体在到达当前目标后会获得新目标。解决LMAPF的常见方法是将其视为一系列MAPF问题，周期性地根据智能体当前配置到当前目标进行重新规划。该方法的一个显著缺陷在于：在MAPF中，所有智能体必须同时到达各自的目标配置，这对LMAPF而言是不必要的限制。已有技术被提出以间接缓解此缺陷。本文描述了这些缓解技术失效的案例。作为替代方案，我们提出通过求解一系列改进的MAPF问题来解决LMAPF问题，其目标是使每个智能体最终访问其目标，但无需所有智能体同时到达。我们将此MAPF变体称为瞬态MAPF（TMAPF），并基于现有MAPF算法提出了若干求解算法。有限的实验评估表明，在某些情况下，采用TMAPF算法替代LMAPF框架中的MAPF算法可显著提升系统吞吐量。