ICRA 2011 Paper Abstract


Paper WeP109.1

Alterovitz, Ron (University of North Carolina at Chapel Hill), Patil, Sachin (UNC Chapel Hill), Derbakova, Anna (University of North Carolina at Chapel Hill)

Rapidly-Exploring Roadmaps: Weighing Exploration vs. Refinement in Optimal Motion Planning

Scheduled for presentation during the Regular Sessions "Motion and Path Planning III" (WeP109), Wednesday, May 11, 2011, 13:40−13:55, Room 5D

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 14, 2020

Keywords Motion and Path Planning, Medical Robots and Systems


Computing globally optimal motion plans requires exploring the configuration space to identify reachable free space regions as well as refining understanding of already explored regions to find better paths. We present the rapidly-exploring roadmap (RRM), a new method for single-query optimal motion planning that allows the user to explicitly consider the trade-off between exploration and refinement. RRM initially explores the configuration space like a rapidly exploring random tree (RRT). Once a path is found, RRM uses a user-specified parameter to weigh whether to explore further or to refine the explored space by adding edges to the current roadmap to find higher quality paths in the explored space. Unlike prior methods, RRM does not focus solely on exploration or refine prematurely. We demonstrate the performance of RRM and the trade-off between exploration and refinement using two examples, a point robot moving in a plane and a concentric tube robot capable of following curved trajectories inside patient anatomy for minimally invasive medical procedures.



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