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：摘要：本文研究了多机器人任务分配与路径规划问题（MRTAPF），其中需要多个智能体访问所有给定的目标位置，同时避免相互碰撞。我们提出了一种新颖的两层算法SA-reCBS，该算法将模拟退火算法与基于冲突的搜索级联起来以解决此问题。与MRTAPF领域的其他方法相比，SA-reCBS的优势在于：无需事先将目标预分组为与机器人数量相同的组别，即可使部分所需智能体沿无碰撞路径访问所有目标。我们在多种仿真实例中测试了该算法，并与现有最优算法进行了比较。结果表明，SA-reCBS在成功率、计算耗时及目标函数值方面均表现出更优异的性能。