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We interleave sampling based motion planning methods with pruning ideas fromminimum spanning tree algorithms to develop a new approach for solving aMulti-Goal Path Finding (MGPF) problem in high dimensional spaces. The approachalternates between sampling points from selected regions in the search spaceand de-emphasizing regions that may not lead to good solutions for MGPF. Ourapproach provides an asymptotic, 2-approximation guarantee for MGPF. We alsopresent extensive numerical results to illustrate the advantages of ourproposed approach over uniform sampling in terms of the quality of thesolutions found and computation speed.

Informed Steiner Trees: Sampling and Pruning for Multi-Goal Path Finding in High Dimensions