Handle URI:
http://hdl.handle.net/10754/597450
Title:
Adapting RRT growth for heterogeneous environments
Authors:
Denny, Jory; Morales, Marco; Rodriguez, Samuel; Amato, Nancy M.
Abstract:
Rapidly-exploring Random Trees (RRTs) are effective for a wide range of applications ranging from kinodynamic planning to motion planning under uncertainty. However, RRTs are not as efficient when exploring heterogeneous environments and do not adapt to the space. For example, in difficult areas an expensive RRT growth method might be appropriate, while in open areas inexpensive growth methods should be chosen. In this paper, we present a novel algorithm, Adaptive RRT, that adapts RRT growth to the current exploration area using a two level growth selection mechanism. At the first level, we select groups of expansion methods according to the visibility of the node being expanded. Second, we use a cost-sensitive learning approach to select a sampler from the group of expansion methods chosen. Also, we propose a novel definition of visibility for RRT nodes which can be computed in an online manner and used by Adaptive RRT to select an appropriate expansion method. We present the algorithm and experimental analysis on a broad range of problems showing not only its adaptability, but efficiency gains achieved by adapting exploration methods appropriately. © 2013 IEEE.
Citation:
Denny J, Morales M, Rodriguez S, Amato NM (2013) Adapting RRT growth for heterogeneous environments. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Available: http://dx.doi.org/10.1109/IROS.2013.6696589.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Nov-2013
DOI:
10.1109/IROS.2013.6696589
Type:
Conference Paper
Sponsors:
This research supported in part by NSF awards CNS-0551685,CCF-0833199,CCF-0830753,IIS-0917266,IIS-0916053,EFRI-1240483,RI-1217991,by NIH NCI R25 CA090301-11, by THECB NHARP award 000512-0097-2009,by Chevron, IBM, Intel, Oracle/Sun, by Award KUS-C1-016-04,made by King Abdullah University of Science and Technology (KAUST), and by Instituto Tecnologico Autonomo de Mexico and Academia Mexicana de Cultura A.C.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorDenny, Joryen
dc.contributor.authorMorales, Marcoen
dc.contributor.authorRodriguez, Samuelen
dc.contributor.authorAmato, Nancy M.en
dc.date.accessioned2016-02-25T12:33:29Zen
dc.date.available2016-02-25T12:33:29Zen
dc.date.issued2013-11en
dc.identifier.citationDenny J, Morales M, Rodriguez S, Amato NM (2013) Adapting RRT growth for heterogeneous environments. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Available: http://dx.doi.org/10.1109/IROS.2013.6696589.en
dc.identifier.doi10.1109/IROS.2013.6696589en
dc.identifier.urihttp://hdl.handle.net/10754/597450en
dc.description.abstractRapidly-exploring Random Trees (RRTs) are effective for a wide range of applications ranging from kinodynamic planning to motion planning under uncertainty. However, RRTs are not as efficient when exploring heterogeneous environments and do not adapt to the space. For example, in difficult areas an expensive RRT growth method might be appropriate, while in open areas inexpensive growth methods should be chosen. In this paper, we present a novel algorithm, Adaptive RRT, that adapts RRT growth to the current exploration area using a two level growth selection mechanism. At the first level, we select groups of expansion methods according to the visibility of the node being expanded. Second, we use a cost-sensitive learning approach to select a sampler from the group of expansion methods chosen. Also, we propose a novel definition of visibility for RRT nodes which can be computed in an online manner and used by Adaptive RRT to select an appropriate expansion method. We present the algorithm and experimental analysis on a broad range of problems showing not only its adaptability, but efficiency gains achieved by adapting exploration methods appropriately. © 2013 IEEE.en
dc.description.sponsorshipThis research supported in part by NSF awards CNS-0551685,CCF-0833199,CCF-0830753,IIS-0917266,IIS-0916053,EFRI-1240483,RI-1217991,by NIH NCI R25 CA090301-11, by THECB NHARP award 000512-0097-2009,by Chevron, IBM, Intel, Oracle/Sun, by Award KUS-C1-016-04,made by King Abdullah University of Science and Technology (KAUST), and by Instituto Tecnologico Autonomo de Mexico and Academia Mexicana de Cultura A.C.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleAdapting RRT growth for heterogeneous environmentsen
dc.typeConference Paperen
dc.identifier.journal2013 IEEE/RSJ International Conference on Intelligent Robots and Systemsen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionInstituto Tecnologico Autonomo de Mexico, Mexico City, Mexicoen
kaust.grant.numberKUS-C1-016-04en
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