实时多智能体路径寻找中的预算分配策略
Budget Allocation Policies for Real-Time Multi-Agent Path Finding
AI总结：
本文提出了一种实时多智能体路径寻找中的预算分配策略，通过智能分配规划预算提升求解效率。
摘要：
多智能体路径寻找（MAPF）是寻找一组智能体路径的问题，使得每个智能体能够到达其目标地点同时避免与其他智能体碰撞。这个问题在许多机器人应用中出现，如自动化仓库和无人机群。许多MAPF求解器是为离线设计的，即首先为所有智能体生成路径然后执行。在现实世界中，等待完整解决方案后再允许任何机器人移动通常是不实际的。实时MAPF（RT-MAPF）捕捉了这种设置，假设智能体必须在固定规划期后开始执行，并执行固定数量的动作，称为执行窗口。这导致了一个迭代过程，其中短计划被执行，而下一个执行窗口同时被规划。现有的RT-MAPF解决方案在每个规划期内迭代调用窗口版本的MAPF算法，而没有明确考虑规划预算的大小。我们填补了这一空白，并探索了不同分配规划预算的策略，用于窗口版本的MAPF-LNS2，这是一种最先进的MAPF算法。我们的探索显示，基线方法中所有智能体从共享规划预算池中获取是无效的，在挑战性场景中。相反，能够智能地将规划预算分配给智能体的策略能够更快速地解决更多问题实例。
英文摘要：
Multi-Agent Path finding (MAPF) is the problem of finding paths for a set of agents such that each agent reaches its desired destination while avoiding collisions with the other agents. This problem arises in many robotics applications, such as automated warehouses and swarms of drones. Many MAPF solvers are designed to run offline, that is, first generate paths for all agents and then execute them. In real-world scenarios, waiting for a complete solution before allowing any robot to move is often impractical. Real-time MAPF (RT-MAPF) captures this setting by assuming that agents must begin execution after a fixed planning period, referred to as the planning budget, and execute a fixed number of actions, referred to as the execution window. This results in an iterative process in which a short plan is executed, while the next execution window is planned concurrently. Existing solutions to RT-MAPF iteratively call windowed versions of MAPF algorithms in every planning period, without explicitly considering the size of the planning budget. We address this gap and explore different policies for allocating the planning budget in windowed versions of MAPF-LNS2, a state-of-the-art MAPF algorithm. Our exploration shows that the baseline approach in which all agents draw from a shared planning budget pool is ineffective in challenging scenarios. Instead, policies that intelligently distribute the planning budget among agents are able to solve more problem instances in less time.
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。
