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    This study enhances robot arm positioning accuracy by introducing a novel ensemble regularization method to prevent overfitting during kinematic calibration. Experiments confirm significant improvements in precision for industrial automation.

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    Area of Science:

    • Robotics
    • Industrial Automation
    • Machine Learning

    Background:

    • Robot arms are crucial for industrial production, but machining and assembly tolerances degrade their positioning accuracy.
    • Precise robot operation necessitates calibrating kinematic parameters to correct positioning errors.

    Purpose of the Study:

    • To address overfitting issues in robot kinematic calibration.
    • To propose and evaluate novel regularization schemes for improved positioning accuracy.

    Main Methods:

    • Discussed six regularization schemes: L1, L2, dropout, elastic, log, and swish.
    • Developed an ensemble regularization scheme combining six methods to mitigate overfitting.
    • Utilized experimental data to calibrate robot kinematic parameters.

    Main Results:

    • The proposed ensemble regularization scheme effectively avoids overfitting in robot positioning error identification.
    • Significant improvements in robot positioning accuracy were achieved post-calibration.
    • Experimental validation confirmed the feasibility and effectiveness of the developed method.

    Conclusions:

    • The ensemble regularization approach offers a reliable solution for accurate robot kinematic calibration.
    • This method enhances the precision of robot arms in industrial applications.
    • The findings support the broader adoption of advanced calibration techniques in automated manufacturing.