通过空间聚类实现多机器人任务分配与避碰一体化，显著提升效率和安全性。
Multi-Robot Task Allocation for Homogeneous Tasks with Collision Avoidance via Spatial Clustering
- 将工作区划分为若干区域，用K-means聚类分配任务并优化路径。
- 相比最优对比方法，耗时减少93%（1.24秒对17.62秒），任务质量提升7%。
- 完全消除碰撞点，适合工业场景中高效安全的多机器人协同作业。
本文提出一种新框架，针对工业环境中同质测量任务的多机器人任务分配与避碰问题，实现联合求解。通过空间聚类将工作区划分为可区分的操作区域，使每台机器人在独立区域内执行任务并避免冲突。采用K-means聚类划分任务点，并利用2-Opt算法优化各集群内的机器人路径。实验表明，该框架性能优异：相较于最佳对比方法，计算时间减少93%（1.24秒对17.62秒），任务质量提升达7%；同时彻底消除了对比方法中存在的碰撞点。理论分析证实，在大量相同任务分布于稀疏地理区域的条件下，空间分区可统一任务分配与避碰这两个看似分离的问题。研究成果对需兼顾计算效率与无碰撞运行的实际应用具有重要意义。
原文摘要 · Abstract (English)
In this paper, a novel framework is presented that achieves a combined solution based on Multi-Robot Task Allocation (MRTA) and collision avoidance with respect to homogeneous measurement tasks taking place in industrial environments. The spatial clustering we propose offers to simultaneously solve the task allocation problem and deal with collision risks by cutting the workspace into distinguishable operational zones for each robot. To divide task sites and to schedule robot routes within corresponding clusters, we use K-means clustering and the 2-Opt algorithm. The presented framework shows satisfactory performance, where up to 93\% time reduction (1.24s against 17.62s) with a solution quality improvement of up to 7\% compared to the best performing method is demonstrated. Our method also completely eliminates collision points that persist in comparative methods in a most significant sense. Theoretical analysis agrees with the claim that spatial partitioning unifies the apparently disjoint tasks allocation and collision avoidance problems under conditions of many identical tasks to be distributed over sparse geographical areas. Ultimately, the findings in this work are of substantial importance for real world applications where both computational efficiency and operation free from collisions is of paramount importance.
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