A Calibrator Fuzzy Ensemble for Highly-Accurate Robot Arm Calibration

• Published in: IEEE transaction on neural networks and learning systems Vol. PP; pp. 1 - 13
• Main Authors: Luo, Xin, Li, Zhibin, Yue, Wenbin, Li, Shuai
• Format: Journal Article
• Language: English
• Published: United States 26.01.2024
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2024.3354080

The absolute positioning accuracy of an industrial robot arm is vital for advancing manufacturing-related applications like automatic assembly, which can be improved via the data-driven approaches to robot arm calibration. Existing data-driven calibrators have illustrated their efficiency in addressing the issue of robot arm calibration. However, they mostly are single learning models that can be easily affected by the insufficient representation of the solution space, therefore, suffering from the calibration accuracy loss. To address this issue, this study proposes a calibrator fuzzy ensemble (CFE) with twofold ideas: 1) implementing eight data-driven calibrators relying on different sophisticated machine learning algorithms for an industrial robot arm, which guarantees the accuracy of individual base models and 2) innovatively developing a fuzzy ensemble of the obtained eight diversified calibrators to obtain impressively high calibration accuracy for an industrial robot arm. Extensive experiments on an ABB IRB120 industrial robot implemented with MATLAB demonstrate that compared with state-of-the-art calibrators, CFE decreases the maximum error at 8.59%. Hence, it has great potential for real applications.