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Vision-based robot path planning with deep learning
Wu P(伍平); Cao Y(曹阳); He YQ(何玉庆); Li DC(李德才)
Department机器人学研究室
Conference Name11th International Conference on Computer Vision Systems, ICVS 2017
Conference DateJuly 10-13, 2017
Conference PlaceShenzhen, China
Author of SourceCity University of Hong Kong Shenzhen Research Institute; et al; Harbin Institute of Technology; Hong Kong University of Science and; Shenzhen Institutes of Advanced Technology, CAS; Technology; TU Wien
Source PublicationComputer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers
PublisherSpringer Verlag
Publication PlaceBerlin
2017
Pages101-111
Indexed ByEI
EI Accession number20174304295027
Contribution Rank1
ISSN0302-9743
ISBN978-3-319-68344-7
KeywordPath Planning Convolutional Neural Network (Cnn) Classification
AbstractIn this paper, a new method based on deep convolutional neural network (CNN) for path planning of robot is proposed, the aim of which is to transform the mission of path planning into a task of environment classification. Firstly, the images of road are collected from cameras installed as required, and then the comprehensive features are abstracted directly from original images through the CNN. Finally, according to the results of classification, the moving direction of robots is exported. In this way, we build an end-to-end recognition system which maps from raw data to motion behavior of robot. Furthermore, experiment has been provided to demonstrate the performance of the proposed method on different roads.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/21076
Collection机器人学研究室
Corresponding AuthorHe YQ(何玉庆)
Affiliation1.Shenyang Jianzhu University, Shenyang, China
2.Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, China
Recommended Citation
GB/T 7714
Wu P,Cao Y,He YQ,et al. Vision-based robot path planning with deep learning[C]//City University of Hong Kong Shenzhen Research Institute; et al; Harbin Institute of Technology; Hong Kong University of Science and; Shenzhen Institutes of Advanced Technology, CAS; Technology; TU Wien. Berlin:Springer Verlag,2017:101-111.
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