多智能体非同步路径搜索的松同步算法
Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions
摘要：
多智能体路径查找（MAPF）确定多个智能体在其各自的起始位置和目标位置之间的无冲突路径的集合。在现有的MAPF规划器的工作空间建模为一个图，A* 为基础的方法已被广泛研究，由于其保证的完整性和解决方案的最优性，并已证明其效率在许多情况下。然而，几乎所有这些基于A* 的方法都假设每个代理同时执行一个操作，因为所有代理都一起启动和停止。本文提出了一种带有异步操作的MAPF（MAPF-AA）的自然概括，其中代理不一定同时启动和停止。的主要贡献的工作是一个建议的方法称为松散同步搜索（LSS），扩展A* 为基础的MAPF规划器处理异步动作。我们表明LSS是完整的，并找到一个最佳的解决方案，如果存在。我们还将LSS与其他现有的MAPF方法相结合，旨在权衡计算效率的最优性。数值结果证实了LSS的性能和所提出的方法的适用性进行了验证，在Robotarium，一个远程访问的群体机器人研究平台。
英文摘要：
Multi-agent path finding (MAPF) determines an ensemble of collision-free paths for multiple agents between their respective start and goal locations. Among the available MAPF planners for workspace modeled as a graph, A*-based approaches have been widely investigated due to their guarantees on completeness and solution optimality, and have demonstrated their efficiency in many scenarios. However, almost all of these A*-based methods assume that each agent executes an action concurrently in that all agents start and stop together. This article presents a natural generalization of MAPF with asynchronous actions (MAPF-AA) where agents do not necessarily start and stop concurrently. The main contribution of the work is a proposed approach called Loosely Synchronized Search (LSS) that extends A*-based MAPF planners to handle asynchronous actions. We show LSS is complete and finds an optimal solution if one exists. We also combine LSS with other existing MAPF methods that aims to trade-off optimality for computational efficiency. Numerical results are presented to corroborate the performance of LSS and the applicability of the proposed method is verified in the Robotarium, a remotely accessible swarm robotics research platform.
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