Randomized motion planning for car-like robots with C-PRM

We propose a new approach for motion planning for nonholonomic car-like robots which is based on a customizable probabilistic roadmap (C-PRM). A major advantage of our approach is that it enables the same roadmap to be efficiently utilized for car-like robots with different turning radii, which need not be known before the query time. Our C-PRM-based approach first builds a so-called control roadmap which does not incorporate any nonholonomic constraints. The control roadmap is used to efficiently generate ´good´ configurations of the car, e.g., aligned with the roadway. The control roadmap is also used to guide the roadmap connection. The paths encoded in the roadmap consist of straight-line segments and arcs, where transitions between the two require full stopping of the car. The control roadmap assists in the optimization and smoothing of these paths using cubic B-splines. Results with a simple car-like robot are very promising