Abstract

We present our current progress on the design and analysis of path planning algorithms based on Rapidly-exploring Random Trees (RRTs). The basis of our methods is the incremental construction fo search trees that attempt to rapidly and uniformly explore the state space, offering benefits that are similar to those obtained by other successful randomized planning methods; however, RRTs are particularly suited for problems that invoke differential constraints. Basic properties of RRTs are established, including convergence to a uniform coverage of nonconvex spaces. Several planners based on RRTs are discussed and compared. Experimental results are presented for planning problems that invoke holonomic constraints for rigid and articulated bodies, manipulation, nonholonomic constraints, kinodynamic constraints, kinematic closure constraints, and up to twelve degrees of freedom. Key open issues and areas of furture research are also discussed.

1997 - 2006 © James Kuffner, Jr.