Please use this identifier to cite or link to this item:
DC FieldValue
dc.titleWorkspace importance sampling for probabilistic roadmap planning
dc.contributor.authorKurniawati, H.
dc.contributor.authorHsu, D.
dc.identifier.citationKurniawati, 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.
dc.description.abstractProbabilistic 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.
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitle2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.