Method of Industrial Robot Accuracy Compensation Based on Particle Swarm Optimization Neural Network

Abstract

For the absolute positioning accuracy of industrial robots used in aircraft flexible automated assembly cannot meet assembly precision,a neural network-based integrated accuracy compensation approach taking into account ambient temperature change factor was proposed based on robot spatial grid accuracy compensation method.Neural network's initial weights and thresholds were optimized by using particle swarm optimization method in order to prevent from falling into local minima in training.The experimental results show that the maximum value of the robot positioning error is as 0.32mm,and the mean value is as0.19mm with the temperature in the range of 20℃ to 30℃,which are much more better than the previous values 1~3mm,the absolute positioning accuracy can satisfy the requirements of aircraft automatic assembly.