Multi-Agent Combinatorial Path Finding (MCPF) seeks collision-free paths for multiple agents from their initial to goal locations, while visiting a set of intermediate target locations in the middle of the paths. MCPF is challenging as it involves both planning collision-free paths for multiple agents and target sequencing, i.e., solving traveling salesman problems to assign targets to and find the visiting order for the agents. Recent work develops methods to address MCPF while minimizing the sum of individual arrival times at goals. Such a problem formulation may result in paths with different arrival times and lead to a long makespan, the maximum arrival time, among the agents. This paper proposes a min-max variant of MCPF, denoted as MCPF-max, that minimizes the makespan of the agents. While the existing methods (such as MS*) for MCPF can be adapted to solve MCPF-max, we further develop two new techniques based on MS* to defer the expensive target sequencing during planning to expedite the overall computation. We analyze the properties of the resulting algorithm Deferred MS* (DMS*), and test DMS* with up to 20 agents and 80 targets. We demonstrate the use of DMS* on differential-drive robots.

翻译：多智能体组合路径规划（MCPF）旨在为多个智能体规划从初始位置到目标位置的无碰撞路径，同时要求智能体在路径中访问一组中间目标位置。MCPF 具有挑战性，因为它既涉及为多个智能体规划无碰撞路径，又涉及目标序列规划，即通过求解旅行商问题来为智能体分配目标并确定访问顺序。现有研究已开发出在最小化各智能体到达目标位置时间总和的前提下求解 MCPF 的方法。此类问题建模可能导致各智能体路径的到达时间不同，并产生较长的最大完工时间（即所有智能体中的最晚到达时间）。本文提出 MCPF 的一个最小-最大变体，称为 MCPF-max，其目标是最小化智能体的最大完工时间。虽然现有 MCPF 求解方法（如 MS*）可经调整用于求解 MCPF-max，但我们进一步基于 MS* 开发了两种新技术，通过在规划过程中推迟代价高昂的目标序列计算来加速整体求解。我们分析了所得算法——延迟 MS*（DMS*）——的性质，并在最多 20 个智能体和 80 个目标的环境中对 DMS* 进行了测试。最后，我们在差速驱动机器人上演示了 DMS* 的应用。