Improvement of Accuracy Performance for 6R Serial Industrial Robot Based on Data-Driven
With the deepening of the application of industrial robots in the high-end manufacturing field,the problem of low absolute positioning accuracy has become increasingly prominent.In this paper,the problem of robot pose error prediction based on neural network is studied.The kinematic modeling and error analysis of Staubli TX60 series industrial robot are carried out.A robot measurement experiment platform based on Leica AT960 laser tracker is built,and a large number of end position error data are measured and calculat-ed.The optimal DNN neural network structure is designed and optimized,and the actual pose error of the robot is predicted by the neural network,which avoids the complicated error modeling in the model-based robot precision improvement method.After compensation,the average absolute position error and average absolute attitude error of the robot decreased from(0.671 12 mm,0.002 86 rad)before compensation to(0.048 58 mm,0.000 46 rad),and the position accuracy increased by 92.76%and the attitude accuracy increased by 83.92%.Finally,through the comparison experiment with BP and Elman neural network,it is verified that the improvement of robot pose accuracy based on deep neural network has better balance.
data drivenindustrial roboticsnon-model calibrationaccuracy performancerobot calibration
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