Abstract
The positioning accuracy of a robot directly affects the quality of its operations. In this study, a calibration method is proposed based on combining a model with least-squares support vector regression (LSSVR) to improve robot positioning accuracy. First, a geometric error model of the robot is established. Second, singular value decomposition (SVD) and physical model analysis method are employed to process the coupling parameters in the error model to improve the accuracy and efficiency of identification. Third, as nongeometric errors hinder the construction of an accurate and complete mathematical model and affect the residual positioning errors of the robot, LSSVR is used to compensate for the residual positioning errors caused by nongeometric errors. The proposed method thus improves the accuracy and robustness of finite sample estimation. Finally, an experiment is performed on an IRB1410 robot with a parallelogram mechanism. The maximum/mean positioning errors of the robot decrease from 2.0348/1.0978 mm to 0.1659/0.0733 mm, and the effectiveness of the proposed method is verified. The proposed method has higher prediction accuracy and stability for small samples than other methods.
Highlights
Robots are typical electromechanical equipment with a wide range of applications in the fields of processing, assembly, and medical treatment
The product of exponentials (POE) and finite and instantaneous screw (FIS) models are abstract descriptions of the transformation of joint motion based on screw theory, which are convenient for calculation and programming but unintuitive and difficult to understand [14]
The linear error of the system is minimized by using the iterative least squares (ILS) method shown in Figure 3 to identify the model parameters: gk gk 1 gk where gk is the parameter updated upon the kth iteration and gk is the parameter error updated upon the kth iteration, which is expressed by the following equation:
Summary
Robots are typical electromechanical equipment with a wide range of applications in the fields of processing, assembly, and medical treatment. Processing tolerances, assembly errors, flexible deformation, etc., result in inconsistencies between actual robot models and the nominal model, which deteriorate the robot absolute positioning accuracy [1,2,3,4] One solution to this problem is to calibrate the robot kinematics before they are utilized. The POE and FIS models are abstract descriptions of the transformation of joint motion based on screw theory, which are convenient for calculation and programming but unintuitive and difficult to understand [14] Measurement equipment, such as laser trackers, are used to obtain pose information for an end-effector [18,19]. A calibration method based on combining a model with LSSVR is proposed to compensate robot geometric and nongeometric errors. The correctness and effectiveness of the proposed method are verified by performing experiments on the robot calibration system and comparing the results with those of other methods
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