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Sampling-based planning methods often become inefficient due to narrowpassages. Narrow passages induce a higher runtime, because the chance to samplethem becomes vanishingly small. In recent work, we showed that narrow passagescan be approached by relaxing the problem using admissible lower-dimensionalprojections of the state space. Those relaxations often increase the volume ofnarrow passages under projection. Solving the relaxed problem is oftenefficient and produces an admissible heuristic we can exploit. However, given abase path, i.e. a solution to a relaxed problem, there are currently notailored methods to efficiently exploit the base path. To efficiently exploitthe base path and thereby its admissible heuristic, we develop sectionpatterns, which are solution strategies to efficiently exploit base paths inparticular around narrow passages. To coordinate section patterns, we developthe pattern dance algorithm, which efficiently coordinates section patterns toreactively traverse narrow passages. We combine the pattern dance algorithmwith previously developed multilevel planning algorithms and benchmark them onchallenging planning problems like the Bugtrap, the double L-shape, an egressproblem and on four pregrasp scenarios for a 37 degrees of freedom shadow handmounted on a KUKA LWR robot. Our results confirm that section patterns areuseful to efficiently solve high-dimensional narrow passage motion planningproblems.

Section Patterns: Efficiently Solving Narrow Passage Problems in Multilevel Motion Planning