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政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 > Item 140.119/14988

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題名:	An Incremental Learning Approach to Motion Planning with Roadmap Management
作者:	李蔡彥;SHIE, YANG-CHUAN
關鍵詞:	incremental learning;motion planning;probabilistic roadmap management;reconfigurable random forest;planning for dynamic environments
日期:	2007-03
上傳時間:	2008-12-16 16:41:29 (UTC+8)
摘要:	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.
關聯:	Journal of Information Science and Engineering, 23(2), 525-238
資料類型:	article
顯示於類別:	[資訊科學系] 期刊論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
jise2007.pdf		1352Kb	Adobe PDF2	447	檢視/開啟

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