Probabilistic Analysis of Sampling based Path Planning Algorithms

In this paper we investigate probabilistic completeness and asymptotic optimality of various existing randomized sampling based algorithms such as, probabilistic roadmap methods (PRM) and its many variants. We give new alternate proofs to many such existing theorems regarding probabilistic completeness and asymptotic optimality, in both incremental and independent random problem model framework