Using Path-length Localized RRT-like Search to Solve Challenging Planning Problems
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.
Nathan A. Wedge Michael S. Branicky
Department of Electrical Engineering and Computer Science at Case Western Reserve University,Cleveland,OH 44106 USA
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
3713-3718
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）