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题名: Knowledge-driven path planning for mobile robots: relative state tree
作者: Chen Y(陈洋); Cheng L(程磊); Wu HY(吴怀宇); Zhao XG(赵新刚); Han JD(韩建达)
作者部门: 机器人学研究室
关键词: Path planning ; Relative state tree ; Hierarchical tree ; Incremental learning ; Autonomous planning
刊名: Soft Computing
ISSN号: 1432-7643
出版日期: 2015
卷号: 19, 期号:3, 页码:763-773
收录类别: SCI ; EI
产权排序: 2
摘要: Path planning is important in the field of mobile robot. However, traditional path planning techniques optimize the navigation path solely based on the models of the robot and the environments. Owing to the time-varying environment, the robot is expected to launch the replanning procedure in real-time continuously. It is slow and wastes computing resources for repeated decisions. In this study, a new perspective is adopted which utilizes a knowledge-driven approach for path planning. The concept of relative state tree is proposed to develop an incremental learning method based on a path planning knowledge base. The knowledge library, which stores a collection of the mappings from environmental information to robot decisions, can be established by offline or online learnings. As the robot plans online, its movement is guided by the optimal decision that is retrieved from the library based on the information which matches mostly the current environment. A large number of simulations are executed to verify the proposed method. When comparing to $k$-d tree, this novel method has shown to use smaller storage space and have higher efficiency.
语种: 英语
WOS记录号: WOS:000351409400017
WOS标题词: Science & Technology ; Technology
类目[WOS]: Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
关键词[WOS]: TIME OBSTACLE AVOIDANCE ; ALGORITHM ; OPTIMIZATION
研究领域[WOS]: Computer Science
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/14748
Appears in Collections:机器人学研究室_期刊论文

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