• Login
    View Item 
    •   Home
    • IRAC Institute for Research in Applicable Computing - to April 2016
    • Centre for Research in Distributed Technologies (CREDIT)
    • View Item
    •   Home
    • IRAC Institute for Research in Applicable Computing - to April 2016
    • Centre for Research in Distributed Technologies (CREDIT)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UOBREPCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalDepartmentThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalDepartment

    My Account

    LoginRegister

    About

    AboutLearning ResourcesResearch Graduate SchoolResearch InstitutesUniversity Website

    Statistics

    Display statistics

    K-order surrounding roadmaps path planner for robot path planning

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Authors
    Li, Yueqiao
    Li, Dayou
    Maple, Carsten
    Yue, Yong
    Oyekan, John O.
    Issue Date
    2014-09
    Subjects
    robot path planning
    high-quality roadmaps
    sample-based path planning
    path planning
    robotics
    K-order
    
    Metadata
    Show full item record
    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
    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
    ae974a485f413a2113503eed53cd6c53
    10.1007/s10846-013-9861-3
    Scopus Count
    Collections
    Centre for Research in Distributed Technologies (CREDIT)

    entitlement

     
    DSpace software (copyright © 2002 - 2021)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.