Abstract
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.
Highlights
With high generality and industrial flexibility, robots have been widely used in modern manufacturing, which play an important role in the fields of automobile manufacturing, logistics, machinery manufacture, and so forth
About 90% position errors are caused by the inaccuracy of the kinematic parameters in the controller [1]. e main way to improve the position accuracy of industrial robots is kinematic calibration by which the structural parameters errors due to the tolerance in machining and assembly of robots can be identified and compensated [2]. ere are two levels for kinematic calibration. e first level is to identify and compensate the errors between the transducer reading of joints and the actual joint angle. e second level is to identify and compensate all the kinematic parameters of robots
The kinematic calibration of robots can be classified into four steps [3]: modelling, measurement, parameter identification, and compensation. e kinematic parameter identification of industrial robots is a high nonlinear problem
Summary
Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China. 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. 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. 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
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