校准
机械臂
计算机科学
机器人
模糊逻辑
人工智能
数学
统计
作者
Xin Luo,Zhibin Li,Wenbin Yue,Shuai Li
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-13
被引量:2
标识
DOI:10.1109/tnnls.2024.3354080
摘要
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.
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