| Learning-based force servoing control of a robot with vision in an unknown environment |
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| 作者姓名： | 肖南峰 |
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| 作者单位： | Xiao Nanfeng School of Computer Science and Engineering，South China University of Technology，Guangzhou 510641，P. R. China |
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| 基金项目： | This project was supported by the research foundation of China Education Ministry for the scholars from abroad (2002247). |
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| 摘 要： | A learning-based control approach is presented for force servoing of a robot with vision in an unknown environment. Firstly, mapping relationships between image features of the servoing object and the joint angles of the robot are derived and learned by a neural network. Secondly, a learning controller based on the neural network is designed for the robot to trace the object. Thirdly, a discrete time impedance control law is obtained for the force servoing of the robot, the on-line learning algorithms for three neural networks are developed to adjust the impedance parameters of the robot in the unknown environment. Lastly, wiping experiments are carried out by using a 6 DOF industrial robot with a CCD camera and a force/torque sensor in its end effector, and the experimental results confirm the effecti veness of the approach.
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Learning-based force servoing control of a robot with vision in an unknown environment |
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| Xiao Nanfeng School of Computer Science and Engineering,South China University of Technology,Guangzhou ,P. R. China.Learning-based force servoing control of a robot with vision in an unknown environment[J].Journal of Systems Engineering and Electronics,2004,15(2). |
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| Authors: | Xiao Nanfeng School of Computer Science and Engineering South China University of Technology Guangzhou P R China |
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| Institution: | School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, P. R. China |
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| Abstract: | A learning-based control approach is presented for force servoing of a robot with vision in an unknown environment. Firstly, mapping relationships between image features of the servoing object and the joint angles of the robot are derived and learned by a neural network. Secondly, a learning controller based on the neural network is designed for the robot to trace the object. Thirdly, a discrete time impedance control law is obtained for the force servoing of the robot, the on-line learning algorithms for three neural networks are developed to adjust the impedance parameters of the robot in the unknown environment. Lastly, wiping experiments are carried out by using a 6 DOF industrial robot with a CCD camera and a force/torque sensor in its end effector, and the experimental results confirm the effecti veness of the approach. |
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| Keywords: | robotics force servoing vision control learning algorithm neural network |
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