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：本文研究了多机器人任务分配与路径发现问题（MRTAPF），即要求多个智能体在避免相互碰撞的同时访问所有给定的目标位置。我们提出了一种新颖的两层算法SA-reCBS，该算法通过级联模拟退火算法与基于冲突的搜索来解决该问题。与MRTAPF领域其他方法相比，SA-reCBS的优势在于：无需预先将目标点按机器人数量捆绑为相同数量的组别，即可使部分所需智能体在无碰撞路径上访问所有目标。我们在多种仿真实例中测试了该算法，并与当前最优算法进行了比较。结果表明，SA-reCBS在更高成功率、更短计算时间和更优目标值方面表现出更优性能。