This paper presents a wide-range routing method for multiple rovers deployed for a lunar polar exploration mission. In the mission scenario considered in this paper, multiple exploration points are divided into several sets of points to be assigned to multiple rovers. Subsequently, routes between the exploration points are generated for each rover. This assignment and routing problems are well known as the multiple Traveling Salesman Problem (mTSP). The scenario aims to minimize two objective functions: the sum and standard deviation of operation time among the multiple rovers. This problem is then subject to the multi-objective mTSP (MOmTSP). Therefore, the proposed method is composed of a three-step procedure for solving the MOmTSP. First, the k-means algorithm divides an exploration area into several regions for the number of rovers. Subsequently, a routing algorithm with the space-filling curve and 2-opt algorithm determines the order of exploration points in each region. A local path planner then generates a feasible path between two exploration points. The simulation results using the proposed method with 80 cases are statistically analyzed. We confirm that the proposed method solves the multiple optimization problems by finding the routes that can equalize the rover's operation time and minimize their deviations.

本文提出了一种为月球极地探测任务部署的多个漫游车的大范围路由方法。在本文考虑的任务场景中，多个探索点被分成几组点分配给多个流动站。随后，为每个流动站生成探索点之间的路线。这种分配和路由问题被称为多重旅行商问题 (mTSP)。该场景旨在最小化两个目标函数：多个流动站之间运行时间的总和和标准差。这个问题然后受制于多目标 mTSP (MOmTSP)。因此，所提出的方法由求解 MOmTSP 的三步程序组成。首先，k-means 算法根据流动站的数量将一个探索区域划分为几个区域。随后，具有空间填充曲线的路由算法和 2-opt 算法确定每个区域中探索点的顺序。然后，本地路径规划器会在两个探索点之间生成一条可行路径。对80个案例使用该方法的模拟结果进行了统计分析。我们确认所提出的方法通过找到可以均衡流动站运行时间并最小化其偏差的路线来解决多个优化问题。