On the probabilistic completeness of the sampling-based feedback motion planners in belief space
Ali–akbar Agha–mohammadi, Suman Chakravorty, Nancy M. Amato · 2012
This paper extends the concept of “probabilistic completeness” defined for motion planners in state space (or configuration space) to the concept of “probabilistic completeness under uncertainty” for motion planners in belief space. Accordingly, an approach is proposed to verify the probabilistic completeness of the sampling-based planners in belief space. Finally, through the proposed approach, it is shown that under mild conditions the sampling-based methods constructed based on the abstract framework of FIRM (Feedback-based Information Roadmap Method) are probabilistically complete under uncertainty.