知情Steiner树：高维多目标路径搜索的抽样和修剪
Informed Steiner Trees: Sampling and Pruning for Multi-Goal Path Finding in High Dimensions
摘要：
我们交错采样为基础的运动规划方法与修剪思想，从最小生成树算法开发一种新的方法来解决多目标路径查找（MGPF）问题在高维空间。该方法在来自搜索空间中的所选区域的采样点和不强调可能不会导致MGPF的良好解决方案的区域之间交替。我们的方法提供了一个渐进的，2-近似保证MGPF。我们还提出了广泛的数值结果来说明我们提出的方法的优点，均匀采样的质量的解决方案和计算速度。
英文摘要：
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.
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