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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 efficient in solving high dimensional problems. Eventhough several RRT variants have been proposed for dynamic replanning, thesemethods only perform well in environments with infrequent changes. This paperaddresses the dynamic path planning problem by combining simple techniques in amulti-stage probabilistic algorithm. This algorithm uses RRTs for initialplanning and informed local search for navigation. We show that thiscombination of simple techniques provides better responses to highly dynamicenvironments than the RRT extensions.

A Multi-stage Probabilistic Algorithm for Dynamic Path-Planning