发表机构
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对通用地图的终身多智能体路径发现问题，提出PUSH规划器，结合多种框架优势实现可扩展长时程规划，在10k智能体场景下吞吐量优于基线方法
AI 中文摘要
终身多智能体路径发现（Lifelong Multi-Agent Path Finding, LMAPF）需要在严格的实时约束下为大规模智能体集群生成无碰撞路径。PIBT和增强型PIBT（Enhanced PIBT, EPIBT）等反应式框架通过基于规则的逐步协调可轻松扩展至数千个智能体，但存在严重的时间短视问题，在需要长时程推理的场景中效果不佳。RHCR在多步时程上规划窗口路径，但会产生大量规划开销，阻碍了可扩展性。TP通过在每个时间步仅规划部分智能体来应对上述两个挑战，但其适用性仅限于高度结构化的地图。为在通用地图上实现可扩展的长时程规划，我们提出了交错时程路径更新（Path Updates over Staggered Horizons, PUSH），这是一种LMAPF规划器，能够在多步时程规划的同时，在一秒内协调数千个智能体。PUSH结合了PIBT、RHCR和TP的关键优势：与TP类似，PUSH通过在每个时间步使用交错规划窗口仅规划部分智能体来降低计算复杂度；但与TP不同，PUSH在通用地图上规划RHCR风格的窗口路径，无需依赖限制性的地图假设。为在拥堵环境中保持高吞吐量，PUSH还将受EPIBT启发的优先级继承、回溯和任何时间改进方法集成到其窗口规划中。在两个需要长时程推理的真实MAPF场景中的实证评估表明，PUSH可扩展至与EPIBT相同的大规模智能体负载（例如10000个智能体），同时实现比所有基线方法显著更高的系统吞吐量。
英文摘要
Lifelong Multi-Agent Path Finding (LMAPF) requires generating collision-free paths for large agent fleets under strict real-time constraints. Reactive frameworks such as PIBT and Enhanced PIBT (EPIBT) scale effortlessly to thousands of agents through rule-based, step-by-step coordination but suffer from severe temporal myopia, making them ineffective in scenarios where long-horizon reasoning is essential. RHCR plans windowed paths over multi-step horizons but incurs substantial planning overheads that hinder scalability. TP tackles both challenges by planning only subsets of agents at each timestep, yet its applicability is restricted to highly structured maps. To achieve long-horizon planning at scale across general maps, we propose Path Updates over Staggered Horizons (PUSH), a LMAPF planner capable of coordinating thousands of agents in under a second while planning over multi-step horizons. PUSH combines the key advantages of PIBT, RHCR, and TP. Like TP, PUSH reduces computational complexity by planning only a subset of agents at each timestep using staggered planning windows. Unlike TP, however, PUSH plans RHCR-style windowed paths in general maps without relying on restrictive map assumptions. To maintain high throughput in congested environments, PUSH further integrates EPIBT-inspired priority inheritance, backtracking, and anytime improvements into its windowed planning. Empirical evaluations across two realistic MAPF scenarios requiring long-horizon reasoning show that PUSH scales to the same massive agent loads as EPIBT (e.g., 10k agents) while achieving significantly higher system throughput than all baselines.
Comments11 pages, 6 figures
