Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/39297
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dc.titleOn the probabilistic foundations of probabilistic roadmap planning
dc.contributor.authorHsu, D.
dc.contributor.authorLatombe, J.-C.
dc.contributor.authorKurniawati, H.
dc.date.accessioned2013-07-04T07:38:28Z
dc.date.available2013-07-04T07:38:28Z
dc.date.issued2007
dc.identifier.citationHsu, D.,Latombe, J.-C.,Kurniawati, H. (2007). On the probabilistic foundations of probabilistic roadmap planning. Springer Tracts in Advanced Robotics 28. ScholarBank@NUS Repository.
dc.identifier.issn16107438
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39297
dc.description.abstractWhy are probabilistic roadmap (PRM) planners "probabilistic"? This paper tries to establish the probabilistic foundations of PRM planning and reexamines previous work in this context. It shows that the success of PRM planning depends mainly and critically on the assumption that the configuration space C of a robot often verifies favorable "visibility" properties that are not directly dependent on the dimensionality of C. A promising way of speeding up PRM planners is to extract partial knowledge on such properties during roadmap construction and use this knowledge to adjust the sampling measure continuously. This paper also shows that the choice of the sampling source - pseudo-random or deterministic - has small impact on a PRM planner's performance, compared to that of the sampling measure. These conclusions are supported by both theoretical arguments and empirical results.
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleSpringer Tracts in Advanced Robotics
dc.description.volume28
dc.identifier.isiutNOT_IN_WOS
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