In the Multi-Agent Path Finding (MAPF) problem, a set of agents moving on a graph must reach their own respective destinations without inter-agent collisions. In practical MAPF applications such as navigation in automated warehouses, where occasionally there are hundreds or more agents, MAPF must be solved iteratively online on a lifelong basis. Such scenarios rule out simple adaptations of offline compute-intensive optimal approaches; and scalable sub-optimal algorithms are hence appealing for such settings. Ideal algorithms are scalable, applicable to iterative scenarios, and output plausible solutions in predictable computation time.

For the aforementioned purpose, this study presents Priority Inheritance with Backtracking (PIBT), a novel sub-optimal algorithm to solve MAPF iteratively. PIBT relies on an adaptive prioritization scheme to focus on the adjacent movements of multiple agents; hence it can be applied to several domains. We prove that, regardless of their number, all agents are guaranteed to reach their destination within finite time when the environment is a graph such that all pairs of adjacent nodes belong to a simple cycle (e.g., biconnected). Experimental results covering various scenarios, including a demonstration with real robots, reveal the benefits of the proposed method. Even with hundreds of agents, PIBT yields acceptable solutions almost immediately and can solve large instances that other established MAPF methods cannot. In addition, PIBT outperforms an existing approach on an iterative scenario of conveying packages in an automated warehouse in both runtime and solution quality.

在多智能体路径查找 (MAPF) 问题中，一组在图上移动的智能体必须在没有智能体间冲突的情况下到达各自的目的地。在自动化仓库导航等实际 MAPF 应用中，偶尔会有数百个或更多代理，必须终身在线迭代求解 MAPF。这种情况排除了对离线计算密集型优化方法的简单适应；因此，可扩展的次优算法对此类设置很有吸引力。理想算法是可扩展的，适用于迭代场景，并在可预测的计算时间内输出合理的解决方案。

出于上述目的，本研究提出了带回溯的优先级继承 (PIBT)，这是一种迭代求解 MAPF 的新型次优算法。PIBT 依靠自适应优先级方案来关注多个智能体的相邻移动；因此它可以应用于多个领域。我们证明，无论它们的数量如何，当环境是一个图时，所有代理都可以保证在有限时间内到达目的地，使得所有相邻节点对都属于一个简单的循环（例如，双连接）。涵盖各种场景的实验结果，包括真实机器人的演示，揭示了所提出方法的好处。即使有数百个代理，PIBT 也几乎可以立即产生可接受的解决方案，并且可以解决其他已建立的 MAPF 方法无法解决的大型实例。此外，