论文标题
多代理地形：通过环境操纵通过环境操纵的高效多代理路径
Multi-Agent Terraforming: Efficient Multi-Agent Path Finding via Environment Manipulation
论文作者
论文摘要
多代理探路（MAPF）关注的是，从一开始就为一组代理商到目标位置的无冲突路径在充满障碍的环境中。 MAPF的典型方法将障碍物的位置视为固定的位置，这限制了它们在自动仓库中的有效性，在这些仓库中，可以通过代理（代表机器人）缓解瓶装瓶装的障碍物（代表豆荚或架子），以缓解瓶颈并引入更短的路线。在这项工作中，我们用可移动的障碍开始对MAPF进行研究。特别是，我们介绍了MAPF的新扩展，我们称之为Terraforming MAPF（TMAPF），其中一些代理负责移动障碍物以清除其他代理商的道路。解决TMAPF非常具有挑战性，因为它不仅需要推理代理之间的碰撞，而且还需要在何时何地移动障碍。我们介绍了两种最先进的算法，CBS和PBS的扩展，以解决TMAPF，并证明它们在静态启动设置下可以始终如一地优于最佳解决方案。
Multi-agent pathfinding (MAPF) is concerned with planning collision-free paths for a team of agents from their start to goal locations in an environment cluttered with obstacles. Typical approaches for MAPF consider the locations of obstacles as being fixed, which limits their effectiveness in automated warehouses, where obstacles (representing pods or shelves) can be moved out of the way by agents (representing robots) to relieve bottlenecks and introduce shorter routes. In this work we initiate the study of MAPF with movable obstacles. In particular, we introduce a new extension of MAPF, which we call Terraforming MAPF (tMAPF), where some agents are responsible for moving obstacles to clear the way for other agents. Solving tMAPF is extremely challenging as it requires reasoning not only about collisions between agents, but also where and when obstacles should be moved. We present extensions of two state-of-the-art algorithms, CBS and PBS, in order to tackle tMAPF, and demonstrate that they can consistently outperform the best solution possible under a static-obstacle setting.
