Joint Deformation Compensation Algorithm for Robust Kinematic Calibration of Serial Robots

Abstract

Calibration is a vital method for improving the performance of robots. While the Denavit-Hartenberg (DH) model is a widely used framework in robot calibration, challenges remain in accurately compensating for joint deformations caused by the robot’s weight or end payload, which negatively impact absolute positioning accuracy. In addition, conventional methods can encounter convergence issues during optimization. This article proposes a robust calibration algorithm that addresses these challenges by leveraging the unique characteristics of the modified DH (MDH) model, considering joint deformation, and the separable nonlinear least squares (SNLS) algorithm. The method utilizes the MDH model’s ability to separate linear and nonlinear parameters, transforming the calibration task into an SNLS optimization problem. This enhances robustness during parameter identification by eliminating partial linear model parameters. The SNLS algorithm also reduces the number of nonlinear parameters to be identified, resulting in fewer iterations and faster convergence, significantly decreasing dependence on initial values. Numerical and experimental results demonstrate the effectiveness of the proposed SNLS algorithm. The method significantly improves robot positioning accuracy, particularly under varying load conditions, from 9.43 to 0.71 mm, a 92.47% improvement. These findings highlight the substantial advances and importance of the proposed calibration algorithm in enhancing robot performance and reliability.