Robot Motion Planning in Dynamic Uncertain Environments

In the real world, mobile robots often operate in dynamic and uncertain environments. Therefore, it is necessary to develop a motion planner capable of real-time planning that also addresses uncertainty concerns. In this paper, a new algorithm, Dynamic AO* (DAO*), is developed for navigation tasks of mobile robots. DAO* not only performs a good anytime behavior and offers a fast replanning framework, but also considers the motion uncertainty. Moreover, by incorporating DAO* with D* Lite, a new planning architecture, DDAO*, is represented to efficiently search in large state spaces. Finally, simulations and experiments are shown to verify the efficiency of the proposed algorithms.

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