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|Title:||Workspace importance sampling for probabilistic roadmap planning|
|Authors:||Kurniawati, H. |
|Citation:||Kurniawati, H.,Hsu, D. (2004). Workspace importance sampling for probabilistic roadmap planning. 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2 : 1618-1624. ScholarBank@NUS Repository.|
|Abstract:||Probabilistic Roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but they behave poorly when a robot's configuration space contains narrow passages. This paper presents workspace importance sampling (WIS), a new sampling strategy for PRM planning. Our main idea is to use geometric information from a robot's workspace as "importance" values to guide sampling in the corresponding configuration space. By doing so, WIS increases the sampling density in narrow passages and decreases the sampling density in wide-open regions. We tested the new planner on rigid-body and articulated robots in 2-D and 3-D environments. Experimental results show that WIS improves the planner's performance for path planning problems with narrow passages.|
|Source Title:||2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)|
|Appears in Collections:||Staff Publications|
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