A Region-Based Strategy for Collaborative Roadmap Construction

Handle URI:
http://hdl.handle.net/10754/597393
Title:
A Region-Based Strategy for Collaborative Roadmap Construction
Authors:
Denny, Jory; Sandström, Read; Julian, Nicole; Amato, Nancy M.
Abstract:
© Springer International Publishing Switzerland 2015. Motion planning has seen much attention over the past two decades. A great deal of progress has been made in sampling-based planning, whereby a planner builds an approximate representation of the planning space. While these planners have demonstrated success inmany scenarios, there are still difficult problems where they lack robustness or efficiency, e.g., certain types of narrow spaces. Conversely, human intuition can often determine an approximate solution to these problems quite effectively, but humans lack the speed and precision necessary to perform the corresponding low-level tasks (such as collision checking) in a timely manner. In this work, we introduce a novel strategy called Region Steering in which the user and a PRM planner work cooperatively to map the space while maintaining the probabilistic completeness property of the PRMplanner. Region Steering utilizes two-way communication to integrate the strengths of both the user and the planner, thereby overcoming the weaknesses inherent to relying on either one alone. In one communication direction, a user can input regions, or bounding volumes in the workspace, to bias sampling towards or away from these areas. In the other direction, the planner displays its progress to the user and colors the regions based on their perceived usefulness.We demonstrate that Region Steering provides roadmap customizability, reduced mapping time, and smaller roadmap sizes compared with fully automated PRMs, e.g., Gaussian PRM.
Citation:
Denny J, Sandström R, Julian N, Amato NM (2015) A Region-Based Strategy for Collaborative Roadmap Construction. Algorithmic Foundations of Robotics XI: 125–141. Available: http://dx.doi.org/10.1007/978-3-319-16595-0_8.
Publisher:
Springer Science + Business Media
Journal:
Algorithmic Foundations of Robotics XI
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
2015
DOI:
10.1007/978-3-319-16595-0_8
Type:
Book Chapter
ISSN:
1610-7438; 1610-742X
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 AbdullahUniversity of Science and Technology (KAUST). J. Denny supported in part by an NSF GraduateResearch Fellowship
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Full metadata record

DC FieldValue Language
dc.contributor.authorDenny, Joryen
dc.contributor.authorSandström, Readen
dc.contributor.authorJulian, Nicoleen
dc.contributor.authorAmato, Nancy M.en
dc.date.accessioned2016-02-25T12:32:17Zen
dc.date.available2016-02-25T12:32:17Zen
dc.date.issued2015en
dc.identifier.citationDenny J, Sandström R, Julian N, Amato NM (2015) A Region-Based Strategy for Collaborative Roadmap Construction. Algorithmic Foundations of Robotics XI: 125–141. Available: http://dx.doi.org/10.1007/978-3-319-16595-0_8.en
dc.identifier.issn1610-7438en
dc.identifier.issn1610-742Xen
dc.identifier.doi10.1007/978-3-319-16595-0_8en
dc.identifier.urihttp://hdl.handle.net/10754/597393en
dc.description.abstract© Springer International Publishing Switzerland 2015. Motion planning has seen much attention over the past two decades. A great deal of progress has been made in sampling-based planning, whereby a planner builds an approximate representation of the planning space. While these planners have demonstrated success inmany scenarios, there are still difficult problems where they lack robustness or efficiency, e.g., certain types of narrow spaces. Conversely, human intuition can often determine an approximate solution to these problems quite effectively, but humans lack the speed and precision necessary to perform the corresponding low-level tasks (such as collision checking) in a timely manner. In this work, we introduce a novel strategy called Region Steering in which the user and a PRM planner work cooperatively to map the space while maintaining the probabilistic completeness property of the PRMplanner. Region Steering utilizes two-way communication to integrate the strengths of both the user and the planner, thereby overcoming the weaknesses inherent to relying on either one alone. In one communication direction, a user can input regions, or bounding volumes in the workspace, to bias sampling towards or away from these areas. In the other direction, the planner displays its progress to the user and colors the regions based on their perceived usefulness.We demonstrate that Region Steering provides roadmap customizability, reduced mapping time, and smaller roadmap sizes compared with fully automated PRMs, e.g., Gaussian PRM.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 AbdullahUniversity of Science and Technology (KAUST). J. Denny supported in part by an NSF GraduateResearch Fellowshipen
dc.publisherSpringer Science + Business Mediaen
dc.titleA Region-Based Strategy for Collaborative Roadmap Constructionen
dc.typeBook Chapteren
dc.identifier.journalAlgorithmic Foundations of Robotics XIen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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