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    政大機構典藏 > 理學院 > 資訊科學系 > 期刊論文 >  Item 140.119/14988
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/14988

    Title: An Incremental Learning Approach to Motion Planning with Roadmap Management
    Authors: 李蔡彥;SHIE, YANG-CHUAN
    Keywords: incremental learning;motion planning;probabilistic roadmap management;reconfigurable random forest;planning for dynamic environments
    Date: 2007-03
    Issue Date: 2008-12-16 16:41:29 (UTC+8)
    Abstract: Traditional approaches to the motion-planning problem can be classified into solutions
    for single-query and multiple-query problems with the tradeoffs on run-time computation
    cost and adaptability to environment changes. In this paper, we propose a novel
    approach to the problem that can learn incrementally on every planning query and effectively
    manage the learned road-map as the process goes on. This planner is based on previous
    work on probabilistic roadmaps and uses a data structure called Reconfigurable
    Random Forest (RRF), which extends the Rapidly-exploring Random Tree (RRT) structure
    proposed in the literature. The planner can account for environmental changes while
    keeping the size of the roadmap small. The planner removes invalid nodes in the roadmap
    as the obstacle configurations change. It also uses a tree-pruning algorithm to trim
    RRF into a more concise representation. Our experiments show that the resulting roadmap
    has good coverage of freespace as the original one. We have also successful incorporated
    the planner into the application of intelligent navigation control.
    Relation: Journal of Information Science and Engineering, 23(2), 525-238
    Data Type: article
    Appears in Collections:[資訊科學系] 期刊論文

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