On the Probabilistic Completeness of the Sampling-based Motion Planning Methods Under Uncertainty
نویسندگان
چکیده
This paper extends the concept of probabilistic completeness defined for the motion planners in the absence of noise, to the concept of “probabilistic completeness under uncertainty” for the motion planners that perform planning in the presence of uncertainty. According to the proposed definition, an approach is proposed to verify the probabilistic completeness under uncertainty. Finally, it is shown that the sampling-based method FIRM [1] is a probabilistically complete algorithm under uncertainty.
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Probabilistic Completeness of the Belief Space Motion Planners
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