Building Occupancy Grid Map with Radial Basis Function Neural Network - 文章详情页

作者：

Hou, Yuansong (Hou, Yuansong.) | Ruan, Xiaogang (Ruan, Xiaogang.) | Zhu, Xiaoqing (Zhu, Xiaoqing.) | Li, Cheng (Li, Cheng.)

收录：

EI Scopus

摘要：

Environment　modeling　is　a　main　task　of　exploration　and　autonomous　navigation　problem　for　mobile　robot.　In　this　paper　we　propose　a　continuous　occupancy　map　building　technique　based　on　radial　basis　function　neural　network.　Furthermore,　Bayesian　Committee　Machine　is　applied　to　the　mapping　technique　in　order　to　make　the　mapping　process　computationally　tractable　for　online　application.　Compared　with　the　traditional　occupancy　grid　map,　this　method　provides　a　continuous　model　of　uncertainty　over　map　special　coordinates,　captures　the　natural　statistical　relationship　of　obstacles.　Through　the　simulation　of　the　proposed　method,　it　is　proved　that　the　environment　modeling　is　accurate　and　the　modeling　speed　is　fast.　©　2018　IEEE.

关键词：

Functions Mapping Mobile robots Radial basis function networks

作者机构：

[ 1 ] [Hou, Yuansong]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 2 ] [Hou, Yuansong]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China
[ 3 ] [Ruan, Xiaogang]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 4 ] [Ruan, Xiaogang]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China
[ 5 ] [Zhu, Xiaoqing]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 6 ] [Zhu, Xiaoqing]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China
[ 7 ] [Li, Cheng]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 8 ] [Li, Cheng]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China

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