论文标题
知情的施泰纳树：在高维度中进行多进球路径的抽样和修剪
Informed Steiner Trees: Sampling and Pruning for Multi-Goal Path Finding in High Dimensions
论文作者
论文摘要
我们将基于采样的运动计划方法与从最小跨越树算法的修剪想法进行交换，以开发一种在高维空间中解决多目标路径发现（MGPF）问题的新方法。该方法在搜索空间中选定区域的采样点之间交替，并取消强调可能不会导致MGPF良好解决方案的区域。我们的方法为MGPF提供了渐近的2-抗性保证。我们还提出了广泛的数值结果，以说明我们提出的方法在发现和计算速度的质量方面比均匀采样的优势。
We interleave sampling based motion planning methods with pruning ideas from minimum spanning tree algorithms to develop a new approach for solving a Multi-Goal Path Finding (MGPF) problem in high dimensional spaces. The approach alternates between sampling points from selected regions in the search space and de-emphasizing regions that may not lead to good solutions for MGPF. Our approach provides an asymptotic, 2-approximation guarantee for MGPF. We also present extensive numerical results to illustrate the advantages of our proposed approach over uniform sampling in terms of the quality of the solutions found and computation speed.
