Multipartite RRTs for Rapid Replanning in Dynamic Environments

Abstract— The Rapidly-exploring Random Tree (RRT) algorithm has found widespread use in the field of robot motion planning because it provides a single-shot, probabilistically complete planning method which generalizes well to a variety of problem domains. We present the Multipartite RRT (MPRRT), an RRT variant which supports planning in unknown or dynamic environments. By purposefully biasing the sampling distribution and re-using branches from previous planning iterations, MP-RRT combines the strengths of existing adaptations of RRT for dynamic motion planning. Experimental results show MP-RRT to be very effective for planning in dynamic environments with unknown moving obstacles, replanning in high-dimensional configuration spaces, and replanning for systems with spacetime constraints.

• Type: Conference
• Year: 2007
• Where: ICRA
• Authors: Matthew Zucker, James J. Kuffner, Michael S. Branicky
• Keywords: Dynamic Environments, ICRA 2007, Motion Planning, Robotics, RRT Variant