Choosing good distance metrics and local planners for probabilistic roadmap methods

被引:64
作者
Amato, NM [1 ]
Bayazit, OB [1 ]
Dale, LK [1 ]
Jones, C [1 ]
Vallejo, D [1 ]
机构
[1] Texas A&M Univ, Dept Comp Sci, College Stn, TX 77843 USA
来源
IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION | 2000年 / 16卷 / 04期
基金
美国国家科学基金会;
关键词
distance metrics; local planners; motion planning; probabilistic roadmaps;
D O I
10.1109/70.864240
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a comparative evaluation of different distance metrics and local planners within the context of probabilistic roadmap methods for planning the motion of rigid objects in three-dimensional workspaces. The study concentrates on cluttered three-dimensional workspaces typical of, for example, virtual prototyping applications such as maintainability studies in mechanical CAD designs. Our results include recommendations for selecting appropriate combinations of distance metrics and local planners for such applications. Our study of distance metrics shows that the importance of the translational distance increases relative to the rotational distance as the environment becomes more crowded. We find that each local planner makes some connections than none of the others do-indicating that better connected roadmaps will be constructed using multiple local planners. We propose a new local planning method we call rotate-at-s that often outperforms the common straight-line in C-space method in crowded environments.
引用
收藏
页码:442 / 447
页数:6
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