Probabilistically Safe Corridors to Guide Sampling-Based Motion Planning

In this paper, we introduce a new probabilistically safe local steering primitive for sampling-based motion planning. Our local steering procedure is based on a new notion of a convex probabilistically safe corridor that is constructed around a configuration using tangent hyperplanes of confidence ellipsoids of Gaussian mixture models learned from prior collision history. Accordingly, we propose to expand a random motion planning graph towards a sample configuration using its projection onto probabilistically safe corridors, which efficiently exploits the local geometry of configuration spaces for selecting proper steering direction and adapting steering stepsize. We observe that the proposed local steering procedure generates effective steering motion around complicated regions of configuration spaces, while minimizing collision likelihood.