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Found 2 papers, 1 papers with code
Informed Steiner Trees: Sampling and Pruning for Multi-Goal Path Finding in High Dimensions
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.
S$^*$: A Heuristic Information-Based Approximation Framework for Multi-Goal Path Finding
We combine ideas from uni-directional and bi-directional heuristic search, and approximation algorithms for the Traveling Salesman Problem, to develop a novel framework for a Multi-Goal Path Finding (MGPF) problem that provides a 2-approximation guarantee.
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