Please use this identifier to cite or link to this item: https://doi.org/10.1177/0278364906067174
Title: On the probabilistic foundations of probabilistic roadmap planning
Authors: Hsu, D. 
Latombe, J.-C.
Kurniawati, H. 
Keywords: Motion planning
Pandomized algorithm
Probabilistic roadmap planning
Random sampling
Robotics
Issue Date: 2006
Source: Hsu, D., Latombe, J.-C., Kurniawati, H. (2006). On the probabilistic foundations of probabilistic roadmap planning. International Journal of Robotics Research 25 (7) : 627-643. ScholarBank@NUS Repository. https://doi.org/10.1177/0278364906067174
Abstract: Why is probabilistic roadmap (PRM) planning probabilistic? How does the probability measure used for sampling a robot's configuration space affect the performance of a PRM planner? These questions have received little attention to date. This paper tries to fill this gap and identify promising directions to improve future planners. It introduces the probabilistic foundations of PRM planning and examines previous work in this context. It shows that the success of PRM planning depends mainly and critically on favorable " visibility" properties of a robot's configuration space. A promising direction for speeding up PRM planners is to infer partial knowledge of such properties from both workspace geometry and information gathered during roadmap construction, and to use this knowledge to adapt the probability measure for sampling. 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 with that of the sampling measure. These conclusions are supported by both theoretical and empirical results. © 2006 SAGE Publications.
Source Title: International Journal of Robotics Research
URI: http://scholarbank.nus.edu.sg/handle/10635/39173
ISSN: 02783649
DOI: 10.1177/0278364906067174
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