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CASIA OpenIR
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Nonlinear model predictive position control for a tail-actuated robotic fish 期刊论文
NONLINEAR DYNAMICS, 2020, 页码: 13
作者:  Zhang, Pengfei;  Wu, Zhengxing;  Meng, Yan;  Tan, Min;  Yu, Junzhi
收藏  |  浏览/下载：188/0  |  提交时间：2021/01/07
Bionic robotic fish  Position control  Nonlinear model predictive control  Underactuated dynamics  
Reaction-Wheel-Based Roll Stabilization for a Robotic Fish Using Neural Network Sliding Mode Control 期刊论文
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2020, 卷号: 25, 期号: 4, 页码: 1904-1911
作者:  Zhang, Pengfei;  Wu, Zhengxing;  Dong, Huijie;  Tan, Min;  Yu, Junzhi
Adobe PDF(486Kb)  |  收藏  |  浏览/下载：268/36  |  提交时间：2020/09/28
Mobile robots  Wheels  Rotors  Robot sensing systems  Stability analysis  Mechatronics  Neural network feedforward control  reaction wheel  robotic fish  roll stability  sliding mode control  
Controlling the depth of a gliding robotic dolphin using dual motion control modes 期刊论文
SCIENCE CHINA-INFORMATION SCIENCES, 2020, 卷号: 63, 期号: 9, 页码: 14
作者:  Wang, Jian;  Wu, Zhengxing;  Tan, Min;  Yu, Junzhi
Adobe PDF(847Kb)  |  收藏  |  浏览/下载：282/45  |  提交时间：2020/09/07
gliding robotic dolphin  depth control  dual motion  adaptive control approach  
A Robust Visual Person-Following Approach for Mobile Robots in Disturbing Environments 期刊论文
IEEE SYSTEMS JOURNAL, 2020, 卷号: 14, 期号: 2, 页码: 2965-2968
作者:  Pang, Lei;  Cao, Zhiqiang;  Yu, Junzhi;  Guan, Peiyu;  Chen, Xuechao;  Zhang, Weimin
浏览  |  Adobe PDF(1671Kb)  |  收藏  |  浏览/下载：335/63  |  提交时间：2020/08/03
Mobile robots  Detectors  Cameras  Visualization  Robot vision systems  Kalman filter (KF)  mobile robot  person detector  person-following  reidentification  
Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and Videos 期刊论文
IEEE Transactions on Circuits and Systems for Video Technology, 2020, 卷号: 无, 期号: 无, 页码: 无
作者:  Chen, Xingyu;  Yu, Junzhi;  Kong, Shihan;  Wu, Zhengxing;  Wen, Li
浏览  |  Adobe PDF(4122Kb)  |  收藏  |  浏览/下载：234/56  |  提交时间：2020/06/08
Object detection  Neural networks  Computer vision  Deep learning