Frontier-based exploration on continuous radial basis function neural network map - 文章详情页

作者：

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

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EI Scopus

摘要：

We　present　a　frontier-driven　autonomous　robotic　exploration　method　on　a　continuous　representation　of　environment.　The　approach　utilizes　radial　basis　function　neural　network　to　build　continuous　occupancy　grid　map.　Parametric　frontiers　are　calculated　directly　by　gradient　field　of　occupancy　probability　distribution,　which　clear　show　division　between　free　and　unexplored　space.　Besides,　the　resulting　frontiers　provide　a　measure　of　quality　automatically.　Simulation　is　present　to　show　the　performance　of　the　proposed　technique.　©　2018　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.

关键词：

Mobile robots Probability distributions Functions Radial basis function networks

作者机构：

[ 1 ] [Hou, Yuansong]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100028, China
[ 2 ] [Ruan, Xiaogang]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100028, China
[ 3 ] [Zhu, Xiaoqing]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100028, China
[ 4 ] [Li, Cheng]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100028, China

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