离线时间无关的多智能体路径规划
Offline Time-Independent Multi-Agent Path Planning
AI总结：
本文研究离线时间无关的多智能体路径规划（OTIMAPP）问题，旨在为智能体分配路径使其在异步运行且不共享资源时仍能无阻塞到达目标，并给出了解决条件、计算复杂度、求解器及机器人应用。
摘要：
本文研究了一个不能共享占用资源的多智能体新型规划问题，命名为OTIMAPP（离线时间无关的多智能体路径规划）。给定一个图和一组起点-目标对，该问题在于为每个智能体分配一条路径，使得每个智能体最终都能到达目标且互不阻塞，而无论智能体在运行时如何被调度。其动机源于分布式环境的特性，即智能体完全异步行动，且对其他参与者的确切时间一无所知。我们提出了解决方案条件、计算复杂度、求解器以及机器人应用。
英文摘要：
This paper studies a novel planning problem for multiple agents that cannot share holding resources, named OTIMAPP (Offline Time-Independent Multi-Agent Path Planning). Given a graph and a set of start-goal pairs, the problem consists in assigning a path to each agent such that every agent eventually reaches their goal without blocking each other, regardless of how the agents are being scheduled at runtime. The motivation stems from the nature of distributed environments that agents take actions fully asynchronous and have no knowledge about those exact timings of other actors. We present solution conditions, computational complexity, solvers, and robotic applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
