发表机构
Hokkaido University; Toyota Industries Corporation（北海道大学; 丰田工业株式会社）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对密集仓库在线多智能体取送货任务，提出固定安全港预留机制，实现100%任务完成鲁棒性，相关变体实验揭示了固定返回和撤退中重新分配对性能的影响。
AI 中文摘要
密集仓库通常包含单车道过道、死胡同和树状引导路径，几乎没有空闲智能体等待的空间而不会阻塞其他智能体。现有多智能体取送货（MAPD）算法要保证完成所有有限释放的任务，通常依赖于规划路径可避开的额外等待端点，或双连通拓扑；这些假设在上述布局中可能不成立。本文研究在线MAPD的固定安全港（简称Haven）预留机制，其中取送货任务随时间释放。每个智能体拥有一个固定的安全港，通常是其起始单元格，仅所有者可占用，其他智能体将其视为阻塞区域。对于有限任务释放，我们证明在安全港可达性以及明确的规划/进度假设下，该固定安全港机制可完成所有释放的任务。我们在SHARP（安全港撤退规划器）中实现该机制，该规划器使每个繁忙或撤退的智能体保持在以其安全港为终点的无冲突预留路径上。我们将SHARP与代表性的TP和PIBT系列MAPD基线算法进行比较：令牌传递（TP）、带回溯的优先级继承（PIBT），以及带临时优先级和临时避障的PIBT（PIBTTP-TA），实验在带有附加树状结构的双连通主区域中进行。在鲁棒性扫描中，SHARP是唯一在所有测试配置中成功率达100%的方法，但在树状布局上的集中规划成本显著更高。采用全路径验证的TP式固定返回原点的反事实方法也在测试的树状布局上恢复了鲁棒性，表明固定返回是该处的核心鲁棒性机制。无重写变体显示，在测试的高负载树状条件下，禁用撤退中重新分配会使服务时间（释放到交付的延迟）增加1.89倍，总完成时间增加1.53倍。
英文摘要
Dense warehouses often contain single-lane aisles, dead ends, and tree-like guidepaths that leave little room for idle agents to wait without blocking others. Existing Multi-Agent Pickup and Delivery (MAPD) guarantees for completing all finitely released tasks typically rely on extra waiting endpoints that planned paths can avoid, or on biconnected topology; these assumptions may fail in such layouts. We study fixed-Haven reservation for online MAPD, where pickup-delivery tasks are released over time. Each agent owns a fixed Safe Haven (Haven for short), usually its start cell, that only the owner may occupy and that other agents treat as blocked. For finite task releases, we prove that this fixed-Haven contract completes all released tasks under Haven-Reachability and explicit planning/progress assumptions. We implement the contract in SHARP, a Safe-Haven Retreat Planner that keeps every busy or retreating agent on a collision-free reserved route ending at its Haven. We compare SHARP with representative TP and PIBT-family MAPD baselines: Token Passing (TP), Priority Inheritance with Backtracking (PIBT), and PIBT with Temporary Priority and Temporary Avoidance (PIBTTP-TA) for biconnected main areas with attached trees. In the robustness sweep, SHARP is the only method with 100% success on all tested configurations, at substantially higher centralized planning cost on tree-like layouts. A TP-style fixed-home-return counterfactual with full-route validation also recovers robustness on tested tree-like layouts, suggesting that fixed return is a central robustness mechanism there. A no-overwrite variant shows that disabling mid-retreat reassignment worsens service time (release-to-delivery latency) by 1.89 times and makespan by 1.53 times in the tested high-load tree condition.
Comments11 pages, 9 figures. Accepted at the 14th Workshop on Planning and Robotics (PlanRob), co-located with ICAPS 2026
