2.50
Hdl Handle:
http://hdl.handle.net/10547/336162
Title:
K-order surrounding roadmaps path planner for robot path planning
Authors:
Li, Yueqiao; Li, Dayou; Maple, Carsten; Yue, Yong; Oyekan, John
Abstract:
Probabilistic roadmaps are commonly used in robot path planning. Most sampling-based path planners often produce poor-quality roadmaps as they focus on improving the speed of constructing roadmaps without paying much attention to the quality. Poor-quality roadmaps can cause problems such as poor-quality paths, time-consuming path searching and failures in the searching. This paper presents a K-order surrounding roadmap (KSR) path planner which constructs a roadmap in an incremental manner. The planner creates a tree while answering a query, selects the part of the tree according to quality measures and adds the part to an existing roadmap which is obtained in the same way when answering the previous queries. The KSR path planner is able to construct high-quality roadmaps in terms of good coverage, high connectivity, provision of alternative paths and small size. Comparison between the KSR path planner and Reconfigurable Random Forest (RRF), an existing incremental path planner, as well as traditional probabilistic roadmap (PRM) path planner shows that the roadmaps constructed using the KSR path planner have higher quality that those that are built by the other planners.
Citation:
Li, Y., Li, D., Maple, C., Yue, Y., Wang, Z. and Oyekan, J.(2014) 'K-Order Surrounding Roadmaps Path Planner for Robot Path Planning' Journal of Intelligent & Robotic Systems, Vol 75 (3-4), pp493-516.
Publisher:
Springer
Journal:
Journal of Intelligent & Robotic Systems
Issue Date:
Sep-2014
URI:
http://hdl.handle.net/10547/336162
DOI:
10.1007/s10846-013-9861-3
Additional Links:
http://link.springer.com/10.1007/s10846-013-9861-3
Type:
Article
Language:
en
ISSN:
0921-0296; 1573-0409
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorLi, Yueqiaoen
dc.contributor.authorLi, Dayouen
dc.contributor.authorMaple, Carstenen
dc.contributor.authorYue, Yongen
dc.contributor.authorOyekan, Johnen
dc.date.accessioned2014-11-26T13:41:02Zen
dc.date.available2014-11-26T13:41:02Zen
dc.date.issued2014-09en
dc.identifier.citationLi, Y., Li, D., Maple, C., Yue, Y., Wang, Z. and Oyekan, J.(2014) 'K-Order Surrounding Roadmaps Path Planner for Robot Path Planning' Journal of Intelligent & Robotic Systems, Vol 75 (3-4), pp493-516.en
dc.identifier.issn0921-0296en
dc.identifier.issn1573-0409en
dc.identifier.doi10.1007/s10846-013-9861-3en
dc.identifier.urihttp://hdl.handle.net/10547/336162en
dc.description.abstractProbabilistic roadmaps are commonly used in robot path planning. Most sampling-based path planners often produce poor-quality roadmaps as they focus on improving the speed of constructing roadmaps without paying much attention to the quality. Poor-quality roadmaps can cause problems such as poor-quality paths, time-consuming path searching and failures in the searching. This paper presents a K-order surrounding roadmap (KSR) path planner which constructs a roadmap in an incremental manner. The planner creates a tree while answering a query, selects the part of the tree according to quality measures and adds the part to an existing roadmap which is obtained in the same way when answering the previous queries. The KSR path planner is able to construct high-quality roadmaps in terms of good coverage, high connectivity, provision of alternative paths and small size. Comparison between the KSR path planner and Reconfigurable Random Forest (RRF), an existing incremental path planner, as well as traditional probabilistic roadmap (PRM) path planner shows that the roadmaps constructed using the KSR path planner have higher quality that those that are built by the other planners.en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.urlhttp://link.springer.com/10.1007/s10846-013-9861-3en
dc.rightsArchived with thanks to Journal of Intelligent & Robotic Systemsen
dc.subjectrobot path planningen
dc.subjecthigh-quality roadmapsen
dc.subjectsample-based path planningen
dc.subjectpath planningen
dc.subjectroboticsen
dc.subjectK-orderen
dc.titleK-order surrounding roadmaps path planner for robot path planningen
dc.typeArticleen
dc.identifier.journalJournal of Intelligent & Robotic Systemsen
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