Elasto-geometrical calibration is crucial for enhancing the absolute accuracy of robots in machining operations through the identification and compensation of parameter errors. However, the presence of inconsistent measurement units and improper selection of measuring poses can result in the ill-conditioned identification matrix (ICIM) issue, consequently impacting the accuracy of parameter identification. This paper introduces a novel method to tackle this challenge. Initially, an elasto-geometrical error model is developed based on the orientation-independent measurements (OIM), efficiently reducing the impact of mismatched positions and orientations on the ICIM problem. Subsequently, a PSO-SFFS algorithm is proposed to optimize the measurement configurations and minimize the influence of measurement noise. Furthermore, the incorporation of screw theory and the consideration of parallelogram mechanisms enhance the precision and comprehensiveness of the error model. Subsequent to the development of the error model, calibration comparison experiments are conducted on an industrial robot. Both simulation and experimental results validate the effectiveness of the proposed method in improving parameter identification accuracy.

中文翻译：

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一种提高工业机器人弹性几何标定参数识别精度的新方法


弹性几何校准对于通过参数误差的识别和补偿来提高机器人在加工操作中的绝对精度至关重要。然而，测量单位不一致和测量位姿选择不当会导致病态识别矩阵（ICIM）问题，从而影响参数识别的准确性。本文介绍了一种应对这一挑战的新方法。最初，基于方向无关测量（OIM）开发了弹性几何误差模型，有效减少了位置和方向不匹配对ICIM问题的影响。随后，提出了PSO-SFFS算法来优化测量配置并最小化测量噪声的影响。此外，螺旋理论的结合和平行四边形机构的考虑提高了误差模型的精度和全面性。误差模型开发完成后，在工业机器人上进行了校准比较实验。仿真和实验结果验证了该方法在提高参数辨识精度方面的有效性。