Abstract

We introduce the concept of a Rapidly-exploring Random Tree (RRT) as a randomized data structure that is designed for a broad class of path planning problems. While they share many of the beneficial properties of existing randomized planning techniques, RRTs are specifically designed to handle nonholonomic constraints (including dynamics) and high degrees of freedom. An RRT is iteratively expanded by applying control inputs that drive the system slightly toward randomly-selected points, as opposed to requiring point-to-point convergence, as in the probabilistic roadmap approach. Several desirable properties and a basic implementation of RRTs are discussed. To date, we have successfully applied RRTs to holonomic, nonholonomic, and kinodynamic planning problems of up to twelve degrees of freedom.

Citations

1789	Robot Motion Planning - Latombe - 1991
736	Probabilistic roadmaps for path planning in high-dimensional configuration spaces - Kavraki, Svestka - 1996
324	Randomized kinodynamic planning - LaValle, Kuffner - 2001
307	Robot motion planning: a distributed representation approach - Barraquand, Latombe - 1991
116	A randomized roadmap method for path and manipulation planning - Amato, Wu - 1996
55	Guidelines in nonholonomic motion planning for mobile robots,” in Robot Motion Planning and Control - Laumond, Sekhavat, et al. - 1998
24	Kinodynamic planning - Donald, Xavier, et al. - 1993