Kinematic Calibration of Industrial Robots Based on Distance Information Using a Hybrid Identification Method

To improve the positioning accuracy of industrial robots and avoid using the coordinates of the end effector, a novel kinematic calibration method based on the distance information is proposed. The kinematic model of an industrial robot is established. The relationship between the moving distance of the end effector and the kinematic parameters is analyzed. Based on the results of the analysis and the kinematic model of the robot, the error model with displacements as the reference is built, which is linearized for the convenience of the following identification. The singular value decomposition (SVD) is used to eliminate the redundant parameters of the error model. To solve the problem that traditional optimization algorithms are easily affected by data noise in high dimension identification, a novel extended Kalman filter (EKF) and regularized particle filter (RPF) hybrid identification method is presented. EKF is used in the preidentification of the linearized error model. With the preidentification results as the initial parameters, RPF is used to identify the kinematic parameters of the linearized error model. Simulations are carried out to validate the effectiveness of the proposed method, which shows that the method can identify the error of the parameters and after compensation the accuracy of the robot is improved.

Automated summary

A novel kinematic calibration method based on distance information was proposed to improve the accuracy of industrial robots. By establishing an error model and linearizing it, the method was able to effectively identify and compensate for parameter errors, leading to improved robot accuracy. The method utilized a hybrid identification approach using EKF and RPF to address high dimension identification challenges caused by data noise.