L2-Regularization-Based Kinematic Parameter Identification for Industrial Robots in Limited Measurement Space

Abstract

The accurate identification of kinematic parameters is crucial for improving the positioning accuracy of industrial robots, particularly in advanced manufacturing and automation. However, limited measurement space in practical applications often leads to concentrated data, causing overfitting and unreliable parameter estimation when using traditional identification methods. To address these challenges, this study proposes an L2-regularization-based method to improve parameter identification accuracy by penalizing deviations from the nominal kinematic parameters. The regularization factor is determined using a k-fold cross-validation strategy, ensuring a balance between generalization and accuracy. The proposed method was validated on a six-axis industrial robot, with calibration performed in a constrained measurement space and verification conducted in an expanded workspace. Compared to traditional least-squares methods, which suffer from significant parameter deviations and overfitting, the proposed L2-regularized method effectively improves parameter estimation accuracy. Specifically, this method reduces the mean error from 3.461 mm to 0.399 mm, achieving an approximate 88% improvement compared to the error before calibration. These findings demonstrate the effectiveness of the proposed method in improving parameter identification and positioning accuracy under constrained measurement space.