SA-reCBS：集成反应式路径生成的多机器人任务分配
SA-reCBS: Multi-robot task assignment with integrated reactive path generation
AI总结：
本文提出 SA-reCBS 两层算法，将模拟退火与冲突搜索级联，解决多机器人任务分配与路径规划问题，无需预分组即可实现无碰撞路径，并在成功率、计算时间和目标值上优于现有方法。
摘要：
本文研究多机器人任务分配与路径规划问题（MRTAPF），其中多个智能体需要访问所有给定目标位置，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（Conflict-Based Search）级联起来以解决该问题。与 MRTAPF 领域的其他方法相比，SA-reCBS 的优势在于：无需预先将目标捆绑为与机器人数量相同的分组，即可使一部分智能体在无碰撞路径中访问所有目标。我们在多种仿真实例中测试了该算法，并将其与最先进的算法进行了比较。结果表明，SA-reCBS 具有更高的成功率、更少的计算时间和更优的目标函数值，性能更佳。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
