Abstract
A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation (BP) neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP algorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.
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Hong-Bin Wang received the Ph.D. degree in control theory and control engineering from Yanshan University, PRC in 2005. He is currently a professor in the Institute of Electrical Engineering at Yanshan University, PRC.
His research interests include process automation, robot control technology, variable structure control, and visual servo.
Mian Liu received the M. Sc. degree in control theory and control engineering from Yanshan University, PRC in 2011. She currently works at the 54th China Electronics Technology Group Corporation.
Her research interests include robotics, neural networks, and visual servo.
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Wang, HB., Liu, M. Design of robotic visual servo control based on neural network and genetic algorithm. Int. J. Autom. Comput. 9, 24–29 (2012). https://doi.org/10.1007/s11633-012-0612-x
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DOI: https://doi.org/10.1007/s11633-012-0612-x