Calibration of 6DOF Industrial Manipulator Based on Bionic Optimized Neural Network
Aiming at the positioning accuracy problem of 6DOF industrial manipulator calibration,a manipulator calibration meth-od based on bionic optimization neural network was proposed.The kinematics model of 6DOF industrial manipulator was studied,and its D-H parameters were given.By combining the joint deflection model with the traditional kinematics model calibration technology,the ki-nematics parameters and flexibility parameters of the manipulator could be identified synchronously to improve the positioning accuracy.Then,an artificial neural network was constructed to further compensate the unmodeled errors,such as friction,mechanical transmission error and thermal expansion.In addition,the invasive weed optimization algorithm was used to optimize the weight and bias of neural net-work.Finally,a 6DOF manipulator HX300 was used to test the proposed method,and its feasibility was verified.The research results show that the positioning accuracy of the manipulator is significantly improved after calibration.The average error,maximum error and standard deviation are 0.345 mm,0.637 4 mm,0.162 4 mm,respectively,and they are all smaller than other calibration methods.Com-pared with GA-BP neural network calibration method,the proposed method has better convergence ability,and the average error is re-duced by 15.92％,which is suitable for various high-precision industrial applications.
万方数据
维普
