Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages

Handle URI:
http://hdl.handle.net/10754/599678
Title:
Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages
Authors:
Shi, Kensen; Denny, Jory; Amato, Nancy M.
Abstract:
© 2014 IEEE. Probabilistic RoadMaps (PRMs) have been successful for many high-dimensional motion planning problems. However, they encounter difficulties when mapping narrow passages. While many PRM sampling methods have been proposed to increase the proportion of samples within narrow passages, such difficult planning areas still pose many challenges. We introduce a novel algorithm, Spark PRM, that sparks the growth of Rapidly-expanding Random Trees (RRTs) from narrow passage samples generated by a PRM. The RRT rapidly generates further narrow passage samples, ideally until the passage is fully mapped. After reaching a terminating condition, the tree stops growing and is added to the roadmap. Spark PRM is a general method that can be applied to all PRM variants. We study the benefits of Spark PRM with a variety of sampling strategies in a wide array of environments. We show significant speedups in computation time over RRT, Sampling-based Roadmap of Trees (SRT), and various PRM variants.
Citation:
Shi K, Denny J, Amato NM (2014) Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages. 2014 IEEE International Conference on Robotics and Automation (ICRA). Available: http://dx.doi.org/10.1109/ICRA.2014.6907540.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2014 IEEE International Conference on Robotics and Automation (ICRA)
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
May-2014
DOI:
10.1109/ICRA.2014.6907540
Type:
Conference Paper
Sponsors:
This research supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0916053, IIS-0917266, EFRI-1240483, RI-1217991, by NIH NCI R25 CA090301-11, by Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). J. Denny supported in part by an NSF Graduate Research Fellowship.
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Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorShi, Kensenen
dc.contributor.authorDenny, Joryen
dc.contributor.authorAmato, Nancy M.en
dc.date.accessioned2016-02-28T06:07:20Zen
dc.date.available2016-02-28T06:07:20Zen
dc.date.issued2014-05en
dc.identifier.citationShi K, Denny J, Amato NM (2014) Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages. 2014 IEEE International Conference on Robotics and Automation (ICRA). Available: http://dx.doi.org/10.1109/ICRA.2014.6907540.en
dc.identifier.doi10.1109/ICRA.2014.6907540en
dc.identifier.urihttp://hdl.handle.net/10754/599678en
dc.description.abstract© 2014 IEEE. Probabilistic RoadMaps (PRMs) have been successful for many high-dimensional motion planning problems. However, they encounter difficulties when mapping narrow passages. While many PRM sampling methods have been proposed to increase the proportion of samples within narrow passages, such difficult planning areas still pose many challenges. We introduce a novel algorithm, Spark PRM, that sparks the growth of Rapidly-expanding Random Trees (RRTs) from narrow passage samples generated by a PRM. The RRT rapidly generates further narrow passage samples, ideally until the passage is fully mapped. After reaching a terminating condition, the tree stops growing and is added to the roadmap. Spark PRM is a general method that can be applied to all PRM variants. We study the benefits of Spark PRM with a variety of sampling strategies in a wide array of environments. We show significant speedups in computation time over RRT, Sampling-based Roadmap of Trees (SRT), and various PRM variants.en
dc.description.sponsorshipThis research supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0916053, IIS-0917266, EFRI-1240483, RI-1217991, by NIH NCI R25 CA090301-11, by Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). J. Denny supported in part by an NSF Graduate Research Fellowship.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleSpark PRM: Using RRTs within PRMs to efficiently explore narrow passagesen
dc.typeConference Paperen
dc.identifier.journal2014 IEEE International Conference on Robotics and Automation (ICRA)en
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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