标题:Deep convolutional neural network based unmanned surface vehicle maneuvering
作者:Xu, Qingyang ;Zhang, Chengjin ;Zhang, Li
作者机构:[Xu, Qingyang ;Zhang, Chengjin ;Zhang, Li ] School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai; 264209, China
会议名称:2017 Chinese Automation Congress, CAC 2017
会议日期:October 20, 2017 - October 22, 2017
来源:Proceedings - 2017 Chinese Automation Congress, CAC 2017
出版年:2017
卷:2017-January
页码:878-881
DOI:10.1109/CAC.2017.8242889
摘要:The level of automated unmanned surface vehicle is always dependent on human interactions. An automated collision avoidance approach is proposed which is based on the visual system in order to improve it. Deep convolutional neural network (CNN) is a popular deep neural network for pattern recognition. Three types of encounter scenes are created and recorded which are used as the CNN training samples. The maneuver operations of these samples are conforming to the COLREGs rules. The CNN can predict the maneuvering operation according to the input scene as crewman after the training of CNN, and the central control system can take measures to avoid collision. Different simulations are taken to testify the validity of this approach.
© 2017 IEEE.
收录类别:EI
资源类型:会议论文
TOP