The gaussian PRM sampling for dynamic configuration spaces

Probabilistic roadmap planners (PRMs) are widely used in high dimensional motion planning problems. However, they are less effective in solving narrow passages because feasible configurations in a thin space can rarely be sampled by random. Although some approaches have been proposed, they are either involved in complicated geometrical computations or requiring much information of obstacles. Moreover, if the configuration spaces are dynamic instead of fixed, some solutions may be failure in some unexpected situations. In this work, we provide a novel approach to replace the randomized sampler with the Gaussian PRM sampler. For dynamic configuration spaces, we also invite a machine learning technique to dynamically classify the pre-built samplers for different spaces.