Precision Error Removal in Smart Robotics Using 3D Machine Learning
Ruichun Gu
Journal of Electronic Systems, Vol. 15, No. 4, 2025.
DOI: https://doi.org/10.6025/jes/2025/15/4/203-210
Link: https://www.dline.info/jes/fulltext/v15n4/jesv15n4_3.pdf
Abstract:
The paper â€œError Correction Technology for Welding Robots Based on Three-Dimensional Visual Localizationâ€
by Ruichun Gu presents an innovative approach to improving welding accuracy by integrating 3D
visual localization with welding robots. Traditional welding methods often suffer from seam deviations that
compromise product quality. To address this, the study leverages 3D visual technologies such as laser scanning,
structured light projection, and multi-sensor fusion to capture precise geometric data (position, shape,
size) of weld seams in real time. This data enables immediate error detection and correction during the
welding process. The research incorporates machine learning algorithms, particularly Support Vector Machines
(SVMs), along with geometric features such as curvature and normal vectors, to classify and refine
weld-seam data from 3D point clouds. Experimental results demonstrate high system accuracy, with a
relative error of only 0.38% in seam distance measurements and endpoint positioning errors of 2.5-4 mmâ€”
well within industrial tolerances. The study validates the method on crane main beams using COâ€š shielded
robotic welding. Findings confirm that 3D visual localization significantly enhances welding precision, reduces
defects, and improves production efficiency. The paper concludes that combining advanced visual
sensing with intelligent algorithms offers a promising pathway for next generation welding automation,
with potential for further integration of deep learning and real time adaptive control systems.