An unsupervised adaptive strategy for constructing probabilistic roadmaps

L. Tapia, S. Thomas, B. Boyd, N.M. Amato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


Since planning environments are complex and no single planner exists that is best for all problems, much work has been done to explore methods for selecting where and when to apply particular planners. However, these two questions have been difficult to answer, even when adaptive methods meant to facilitate a solution are applied. For example, adaptive solutions such as setting learning rates, hand-classifying spaces, and defining parameters for a library of planners have all been proposed. We demonstrate a strategy based on unsupervised learning methods that makes adaptive planning more practical. The unsupervised strategies require less user intervention, model the topology of the problem in a reasonable and efficient manner, can adapt the sampler depending on characteristics of the problem, and can easily accept new samplers as they become available. Through a series of experiments, we demonstrate that in a wide variety of environments, the regions automatically identified by our technique represent the planning space well both in number and placement.We also show that our technique has little overhead and that it out-performs two existing adaptive methods in all complex cases studied.© 2009 IEEE.
Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)9781424427888
StatePublished - May 2009
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This research supported in part by NSF Grants EIA-0103742, ACR-0081510, ACR-0113971, CCR-0113974, ACI-0326350, CRI-0551685, CCF-0833199, CCF-0830753, by the DOE, Chevron, IBM, Intel, HP, and by King Abdullah University of Science and Technology (KAUST) Award KUS-C1-016-04. Tapia supported in part by an NIH Molecular Biophysics Training Grant (T32GM065088), a PEO scholarship, and by a Department of Education (GAANN) Fellowship. Thomas supported in part by an NSF Graduate Research Fellowship, a PEO scholarship, a Dept. of Education Graduate Fellowship (GAANN), and an IBM TJ Watson PhD Fellowship.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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