Informed Steiner Trees: Sampling and Pruning for Multi-Goal Path Finding in High Dimensions
This addresses path planning for robotics or autonomous systems in complex environments, representing an incremental improvement by combining existing ideas.
The paper tackles the Multi-Goal Path Finding problem in high-dimensional spaces by interleaving sampling-based motion planning with pruning from minimum spanning trees, achieving an asymptotic 2-approximation guarantee and showing advantages in solution quality and computation speed over uniform sampling.
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.