枢轴与站点多智能体路径寻径：可解性、复杂性与算法
Pivot-and-Station Multi-Agent Path Finding: Solvability, Complexity, and Algorithms
- Politecnico di Torino（都灵理工大学）
- University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对需访问枢轴后停放的多智能体路径寻径问题，研究人员证明其可解性条件、最小化相关时间指标的NP难性，并提出PPP算法，大幅提升基准实例求解效率。
中文摘要 AI 辅助
自动化高密度存储系统（仓库、机器人停车场、工厂物流等）需要智能体车队在稀缺的任务关键资源间移动，随后停放且不阻碍后续操作。我们提出枢轴与站点多智能体路径寻径（Pivot-and-Station Multi-Agent Path Finding，PS-MAPF），这是一种多智能体路径寻径（Multi-Agent Path Finding，MAPF）的变体，其中一部分受任务的智能体必须各访问一组可互换枢轴（如工作站）中的一个，之后整个车队终止于匿名站点，每个站点分配一个智能体。我们完整刻画了可解性：2边连通图上的所有实例均可解；在任意连通图上，相对于未占用顶点数量的结构有效距离度量给出了充要条件。我们证明，即使仅有一个枢轴，最小化站点完成时间（station-makespan）或站点流时间（station-flowtime）也是NP难问题。我们提出三种算法：完整基线算法、基于可满足性问题（SAT）的最优求解器，以及枢轴优先规划（Pivot-Prioritized Planning，PPP），其中PPP求解了74%至89%的基准实例，其完成时间和流时间比基线算法低几个数量级。
英文摘要
Automated high-density storage systems (warehouses, robotic parking, plant logistics, etc.) require fleets of agents to move through scarce task-critical resources and then park without obstructing future operations. We introduce Pivot-and-Station Multi-Agent Path Finding (PS-MAPF), a MAPF variant in which a subset of tasked agents must each visit one of a set of interchangeable pivots (e.g., workstations) before the entire fleet terminates at anonymous stations, one agent per station. We characterize solvability completely: every instance on a 2-edge-connected graph is solvable, and, on arbitrary connected graphs, a structural effective-distance measure relative to the number of unoccupied vertices gives a necessary and sufficient condition. We prove that minimizing station-makespan or station-flowtime is NP-hard already with a single pivot. We present three algorithms, a complete baseline, a SAT-based optimal solver, and Pivot-Prioritized Planning (PPP), the last solving 74-89% of benchmark instances with makespan and flowtime orders of magnitude below the baseline.
