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Probabilistic sampling methods have become very popular to solve single-shotpath planning problems. Rapidly-exploring Random Trees (RRTs) in particularhave been shown to be very efficient in solving high dimensional problems. Eventhough several RRT variants have been proposed to tackle the dynamic replanningproblem, these methods only perform well in environments with infrequentchanges. This paper addresses the dynamic path planning problem by combiningsimple techniques in a multi-stage probabilistic algorithm. This algorithm usesRRTs as an initial solution, informed local search to fix unfeasible paths anda simple greedy optimizer. The algorithm is capable of recognizing when thelocal search is stuck, and subsequently restart the RRT. We show that thiscombination of simple techniques provides better responses to a highly dynamicenvironment than the dynamic RRT variants.

Combining a Probabilistic Sampling Technique and Simple Heuristics to solve the Dynamic Path Planning Problem