Abstract
Probabilistic roadmaps (PRM) are a randomized tool for path planning in configuration spaces where exhaustive search is computationally intractable. It has been noted that the PRM algorithm's computational cost can be greatly reduced by reducing the number of samples necessary to construct a successful roadmap. We examine the information theoretic properties of roadmap construction and propose sampling techniques based upon maximizing the information gain of the roadmap for each configuration sampled. Instead of sampling algorithms which are meant to understand the entirety of configuration space, our sampling is focused on finding configurations which facilitate roadmap construction. We show empirically that these approaches can lead to a significant reduction in the number of samples necessary to construct a useful roadmap.
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