Research on the Robot Wrist Sensor Dynamic Characteristics Based on Improved Genetic Wavelet Neural Networks

Abstract

The multi-dimensional wrist force sensor applied to Motomam V3X robot is introduced. The characteristics of genetic algorithm (GA) and artificial neural networks (ANN) are compared. A novel improved genetic algorithm (IGA) is proposed to train wavelet neural networks (WNN). A kind of new dynamic modeling and dynamic compensation methods are presented based on improved genetic algorithm and wavelet neural networks (IGAWNN) and the principle of algorithm is introduced for the multi-dimensional wrist force sensor. The dynamic model and dynamic compensation model of the robot wrist force sensor are set up according to data of the dynamic calibration, where the structure and parameters of wavelet neural networks of the dynamic model and dynamic compensation are optimized by the improved genetic algorithm. The results show that the proposed methods can overcome the shortcomings of easy convergence to the local minimum points of BP algorithm, and the network complexity, the convergence and the generalization ability are well compromised and the training speed and precision of the new dynamic modeling and dynamic compensation are increased.