基于神经网络视觉伺服的机器人模糊自适应阻抗控制

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Abstract Fuzzy adaptive impedance control strategy is proposed based on neural network visual servoing in unknown environment. Firstly, the one-to-one mapping relations between the image features variation rate of the curved line and the joint angular velocity of the robot are derived. Secondly, an impedance control model is obtained to control the force servoing of the robot, and the impedance model parameters are adjusted fuzzily according to the change of forces in contact to reduce their disturbance in constraint motion. Lastly, the effectiveness of the presented approach is verified by using a 6 DOF robot with a CCD camera and a force/torque sensor installed in its end effector.

Key words： Robot vision impedance control fuzzy control neural network

Received: 13 April 2009 Published: 07 March 2014

PACS:

TP273

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	Li Erchao
	Li Zhanming
	Li Wei

Cite this article:

Li Erchao,Li Zhanming,Li Wei. Robotic Fuzzy Adaptive Impedance Control Based on Neural Network Visual Servoing[J]. Transactions of China Electrotechnical Society, 2011, 26(4): 40-43.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2011/V26/I4/40

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