Multilayer feedforward networks are universal approximators
Neural Networks
An Invitation to 3-D Vision: From Images to Geometric Models
An Invitation to 3-D Vision: From Images to Geometric Models
Implementations of Robot Visual Servo by Learning
ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
Neural network control of multifingered robot hands using visual feedback
IEEE Transactions on Neural Networks
STEP-NC based high-level machining simulations integrated with CAD/CAPP/CAM
International Journal of Automation and Computing
Learning robotic hand-eye coordination through a developmental constraint driven approach
International Journal of Automation and Computing
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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.