Modeling and compensation of measurement errors in hand-eye system for heavy-load industrial robots with line laser sensor

During the continuous scanning process in which a heavy-load robot carries a line laser sensor, measurement accuracy is susceptible to the influence of both geometric errors and joint deformations. Traditional elastogeometric error compensation methods often rely heavily on the calibration accuracy of external measurement systems, which limits their flexibility and precision in on-site applications. To address this limitation, this study proposed Multi-Set Cohesive Calibration (MSCC), a method that eliminates the need for high-precision external system calibration before parameter identification. The MSCC integrated robot geometric errors, compliance errors, and extrinsic parameter errors into a unified error model, solving them collaboratively using multi-configuration measurement data, thereby enhancing the stability and adaptability of the calibration system. Furthermore, to address the high-dimensional and strongly coupled parameter identification problem, a three-stage hybrid optimization algorithm called the Exploration-Annealing-LM (EALM) algorithm was introduced to improve the convergence and global search capability during parameter estimation. The results demonstrated that, in online measurement applications for large structural components, the proposed method achieves an average measurement error of 0.0545 mm and a maximum error of 0.1296 mm, representing reductions of 84.36% and 78.31%, respectively, compared to the uncompensated case.