A hierarchical calibration method for heavy-duty robot with stiffness model and Gaussian process regression model
Aiming at the problem of poor absolute positioning accuracy under heavy loads caused by joint compliance of serial industrial robots,a hierarchical calibration method for heavy-duty industrial robots is proposed.The local product-of-exponential model is used to calibrate the geometric error of the robot.A non-geometric error calibration method based on modeling and machine learning is proposed.In this part,the stiffness model of the robot is first established to calibrate the deformation error,which is a prominent part of the non-geometric error.Then,the re-sidual error is calibrated with the data-driven Gaussian process regression(GPR)model.Experimental results show that this method can effectively improve the absolute positioning accuracy of the robot under load,and the positional accuracy does not fluctuate obviously with the change of load.
industrial robotcalibrationproduct-of-exponentialstiffness modelingGaussian process re-gression(GPR)
万方数据
维普
