Calibration of a Manipulator With a Regularized Parameter Identification Method

As an inverse problem, the parameter identification of manipulators is essential to in-situ calibration. Since the ill-posed inverse kinematic model is sensitive to the measurement value, even tiny errors will make the geometric model of the manipulator wrongly identified. To overcome this problem,...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE access 2022, Vol.10, p.90535-90547
Hauptverfasser: Li, Xuan, Jiang, Wensong, Luo, Zai, Yang, Li, Guo, Bin, Hu, Xiaofeng
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:As an inverse problem, the parameter identification of manipulators is essential to in-situ calibration. Since the ill-posed inverse kinematic model is sensitive to the measurement value, even tiny errors will make the geometric model of the manipulator wrongly identified. To overcome this problem, a Regularized Parameter Identification Method (RPIM) is proposed to calibrate the geometric model of the manipulator. The inverse kinematic model of a 6 DOF manipulator is modified by a Tikhonov regularization to overcome its ill-posed problem. The regularization parameter is optimized by the improved L curve method to adjust the initial model to a well-posed one that approximates the real situation. A calibration system is designed to evaluate the effectiveness of the suggested method. The position of the selected targets is tested by using a laser tracker. The experimental result shows that the absolute position errors of the manipulator are 2.533mm without calibration, 0.472mm by RPIM, 1.445mm by Least Square Method (LSM), 1.353mm by Gradient Descent (GD), and 0.956mm by Gauss-Newton (GN). It shows that the absolute position error of RPIM is reduced by 81.331% after calibration, which is superior to other methods.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3201818