An online prediction and compensation method for robot position errors embedded with error-motion correlation

Abstract

Error prediction and compensation are crucial requirements for improving robot accuracy. To this end, this paper introduces an advanced online method that integrates error-motion correlation for the precise prediction and compensation of robot position errors. The proposed method includes feature selection, model interpretability design, and compensation value smoothness. Analyzing the error characteristics and designing a model based on error-motion correlation reveals that the proposed method reduces position errors by 92 % and 89 % at a frequency of 250 Hz in two tasks with different working conditions. This reduction in position errors ensures smoother compensation, leading to a notable improvement in accuracy for high-precision robotics applications. Unlike existing research that focuses on mapping models, the proposed method prioritizes understanding error behavior, thereby resulting in a comprehensive approach for error management in robotic systems. This contribution is invaluable for advancing the field of precision robotics and ensuring reliable performances in various robotic tasks.