自适应卡尔曼滤波与PSO-GA-BP算法的机器人误差补偿

The cutting holes of a certain type of launcher were machined by a seven-axis robot device clamping laser. In the machining processes due to the low trajectory accuracy and absolute positioning accuracy, it was easy to cause damage and error cutting to the launcher of the model products. The ideal model of the seven-axis robot was established using the D-H algorithm, and the ideal model was verified by the numerical algorithm of forward and inverse kinematics. Based on Sagc-Husa adaptive Kalman filter, the theoretical pose parameters of the ideal model and the measured pose parameters of the laser tracker were used to solve the real pose coordinate information of the seven-axis robots, and the joint errors of the ideal pose parameters and the real pose coordinate information were obtained. Then, the error prediction model of the seven-axis robots was established by combining the PSO-GA-BP joint algorithm. The theoretical pose parameters of the seven-axis robots were used as input samples. The joint angle differences between the real pose and the theoretical pose were taken as the output sample. The joint angle values of the seven-axis robots were compensated according to the model output values by KUKA Robot Workvisual 5.0 software. Through simulation experiments and machining processes, the trajectory errors and absolute positioning errors of the seven-axis robots after the joint error compensation are decreased by 72%, meeting the production requirements.