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Vision-based posture-consistent teleoperation of robotic arm using multi-stage deep neural network
Robotics and Autonomous Systems ( IF 4.3 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.robot.2020.103592
Bin Fang , Xiao Ma , Jiachun Wang , Fuchun Sun

Abstract This paper proposes a visual teleoperation with human–robot posture-consistent based on deep neural network. A multi-stage structure of visual teleoperation network, in which the angles of robotic joints are obtained from human, is deduced. Furthermore, a novel human–robot posture-consistent mapping method is developed to generate dataset of the visual teleoperation network by solving constrained nonlinear matrix functions. Based on the designed framework, the data generator and a well trained multi-stage visual teleoperation network are presented. Finally teleoperation experiments are implemented to demonstrate that the proposed method is effectiveness and reliable.

中文翻译:

使用多级深度神经网络的机械臂基于视觉的姿势一致遥操作

摘要 本文提出了一种基于深度神经网络的人机姿态一致的视觉遥操作。推导出机器人关节角度从人体获取的多级视觉遥操作网络结构。此外,开发了一种新的人机姿势一致映射方法,通过求解受约束的非线性矩阵函数来生成视觉遥操作网络的数据集。基于设计的框架,提出了数据生成器和训练有素的多级视觉遥操作网络。最后通过遥操作实验验证了所提方法的有效性和可靠性。
更新日期:2020-09-01
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