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Description

This paper extends the concept of “probabilistic completeness” defined for the motion planners in the state space (or configuration space) to the concept of “probabilistic completeness under uncertainty” for the motion planners in the belief space. Accordingly, an approach is proposed to verify the probabilistic completeness of the sampling-based planners in the belief space. Finally, through the proposed approach, it is shown that under mild conditions the samplingbased method constructed based on the abstract framework of FIRM (Feedback-based Information-state Roadmap Method) are probabilistically complete under uncertainty.

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