Modern Multi-Agent Path Finding (MAPF) algorithms must plan for hundreds to thousands of agents in congested environments within a second, requiring highly efficient algorithms. Priority Inheritance with Backtracking (PIBT) is a popular algorithm capable of effectively planning in such situations. However, PIBT is constrained by its rule-based planning procedure and lacks generality because it restricts its search to paths that conflict with at most one other agent. This limitation also applies to Enhanced PIBT (EPIBT), a recent extension of PIBT. In this paper, we describe a new perspective on solving MAPF by planning over agent dependencies. Taking inspiration from PIBT's priority inheritance logic, we define the concept of agent dependencies and propose Multi-Dependency PIBT (MD-PIBT) that searches over agent dependencies. MD-PIBT is a general framework where specific parameterizations can reproduce PIBT and EPIBT. At the same time, alternative configurations yield novel planning strategies that are not expressible by PIBT or EPIBT. Our experiments demonstrate that MD-PIBT effectively plans for as many as 10,000 homogeneous agents under various kinodynamic constraints, including pebble motion, rotation motion, and differential drive robots with speed and acceleration limits. We perform thorough evaluations on different variants of MAPF and find that MD-PIBT is particularly effective in MAPF with large agents.

翻译：现代多智能体路径规划（MAPF）算法需在拥挤环境中，于一秒内为数百至数千个智能体规划路径，因此需要高效的算法。优先级继承与回溯（PIBT）是一种能有效应对此类场景的流行算法。然而，PIBT受限于其基于规则的规划流程，且因仅搜索至多与一个其他智能体发生冲突的路径而缺乏通用性。这一局限同样适用于其最新扩展版本增强型PIBT（EPIBT）。本文提出了通过规划智能体依赖关系来解决MAPF问题的新视角。受PIBT优先级继承逻辑启发，我们定义了智能体依赖的概念，并提出了在智能体依赖关系上进行搜索的多依赖PIBT（MD-PIBT）。MD-PIBT是一个通用框架，特定参数化可复现PIBT和EPIBT。同时，替代配置能产生PIBT或EPIBT无法表达的新型规划策略。实验表明，MD-PIBT能在多种运动学约束下（包括卵石运动、旋转运动以及具有速度与加速度限制的差速驱动机器人）高效规划多达10000个同构智能体。我们对不同变体的MAPF进行了全面评估，发现MD-PIBT在处理大型智能体的MAPF问题上尤为有效。