In this paper, we study the multi-robot task assignment and path-finding problem (MRTAPF), where a number of agents are required to visit all given goal locations while avoiding collisions with each other. We propose a novel two-layer algorithm SA-reCBS that cascades the simulated annealing algorithm and conflict-based search to solve this problem. Compared to other approaches in the field of MRTAPF, the advantage of SA-reCBS is that without requiring a pre-bundle of goals to groups with the same number of groups as the number of robots, it enables a part of agents needed to visit all goals in collision-free paths. We test the algorithm in various simulation instances and compare it with state-of-the-art algorithms. The result shows that SA-reCBS has a better performance with a higher success rate, less computational time, and better objective values.

翻译：在本文中，我们研究了多机器人任务分配与路径规划问题（MRTAPF），其中需要多个代理访问所有给定的目标位置，同时避免彼此之间的碰撞。我们提出了一种新颖的两层算法 SA-reCBS，它将模拟退火算法和基于冲突搜索相互结合以解决该问题。与MRTAPF领域中的其他方法相比，SA-reCBS的优点是，在不需要将目标提前分为与机器人数量相同的群组的情况下，它使需要访问所有目标的部分代理能够在避免碰撞的路径中进行。我们在各种仿真实例中测试了该算法，并与现有算法进行了比较。结果表明，SA-reCBS具有更好的性能，具有更高的成功率、更少的计算时间和更好的目标值。