ICRA 2011 Paper Abstract


Paper WeP109.2

Wedge, Nathan (Case Western Reserve University), Branicky, Michael (Case Western Reserve University)

Using Path-Length Localized RRT-Like Search to Solve Challenging Planning Problems

Scheduled for presentation during the Regular Sessions "Motion and Path Planning III" (WeP109), Wednesday, May 11, 2011, 13:55−14:10, 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


Sampling-based planning algorithms of a variety of types have demonstrated pathologically poorly-performing cases, ranging from narrow passages for PRM-based roadmap methods to bug traps for RRT-based tree search methods. This paper introduces an algorithm rooted in the expansion scheme of the RRT that uses local trees to improve performance in difficult cases without sacrificing it in straightforward ones. This method interconnects these local trees, forming a roadmap that is useable for future queries. Additionally, a viable path can be trivially extracted by treating the output as a tree, or one of improved quality can be obtained via discrete search. Experimental data demonstrate performance equal to or better than several other single-query algorithms on two-dimensional test problems and significantly better on two common SE(3) benchmark problems, the flange and the alpha puzzle.



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