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A robotic grasping approach with elliptical cone-based potential fields under disturbed scenes 期刊论文
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2021, 卷号: 18, 期号: 1, 页码: 11
作者:  Geng, Wenjie;  Cao, Zhiqiang;  Li, Zhonghui;  Yu, Yingying;  Jing, Fengshui;  Yu, Junzhi
Adobe PDF(15762Kb)  |  收藏  |  浏览/下载：256/20  |  提交时间：2021/03/29
Robotic grasping  elliptical cone  potential field  disturbed scene  
A monocular vision-based perception approach for unmanned aerial vehicle close proximity transmission tower inspection 期刊论文
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2019, 卷号: 16, 期号: 1, 页码: 20
作者:  Bian, Jiang;  Hui, Xiaolong;  Zhao, Xiaoguang;  Tan, Min
浏览  |  Adobe PDF(1859Kb)  |  收藏  |  浏览/下载：404/91  |  提交时间：2019/07/12
Close proximity inspection of transmission tower  tower localization  UAV self-positioning  monocular vision  
Multi-objective pose optimal distribution method for the feed support system of Five-hundred-meter Aperture Spherical radio Telescope 期刊论文
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2018, 卷号: 15, 期号: 1, 页码: 1-12
作者:  Deng, Sai;  Jing, Fengshui;  Zheng, Rongzhang;  Liang, Zize;  Yang, Guodong
浏览  |  Adobe PDF(928Kb)  |  收藏  |  浏览/下载：282/83  |  提交时间：2018/05/04
Pose Distribution  Multi-objective  Time-varying Barycenter  Stiffness  Fast  
Vision-based autonomous navigation approach for unmanned aerial vehicle transmission-line inspection 期刊论文
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2018, 卷号: 15, 期号: 1, 页码: 1-15
作者:  Hui, Xiaolong;  Bian, Jiang;  Zhao, Xiaoguang;  Tan, Min
浏览  |  Adobe PDF(2695Kb)  |  收藏  |  浏览/下载：404/155  |  提交时间：2018/05/06
Unmanned Aerial Vehicle  Intelligent Inspection  Three-dimensional (3-d) Perception  Visual Navigation  
A PCA-CCA network for RGB-D object recognition 期刊论文
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2018, 卷号: 15, 期号: 1
作者:  Sun, Shiying;  An, Ning;  Zhao, Xiaoguang;  Tan, Min
浏览  |  Adobe PDF(734Kb)  |  收藏  |  浏览/下载：332/102  |  提交时间：2018/10/10
Object Recognition  Pcanet  3d Perception  Canonical Correlation Analysis  Deep Learning