Industrial robots often face challenges in high-precision tasks due to low absolute positioning accuracy. While model-based parameter identification is commonly used for calibration due to its simplicity and cost-effectiveness, it lacks a clear basis for quantifying multifactor influences and conducting sequential error identification. This article proposes a novel stepwise calibration method that leverages sensitivity analysis to address multisource errors. The method identifies primary factors affecting accuracy, evaluates correlated parameters, and conducts sequential identification of errors. Experimental validation on the CR 20 and other robots demonstrates the method’s superior performance in both accuracy and robustness, highlighting its universality and suitability for heavy-load applications. Across all experiments, this method reduces the average error by over 86%, significantly outperforming conventional calibration techniques.
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