一实验统合多种误差，大幅提升工业机器人定位精度。
A Unified Calibration Framework for High-Accuracy Articulated Robot Kinematics
- 用虚拟关节建模几何与非几何误差，单次实验完成识别。
- 实测均值误差26.8μm，优于纯几何校准的102.3μm。
- 适合需要高精度定位的工业机器人校准场景。
研究人员已发现工业机器人工具定位误差的多种来源，并提出了针对性补偿方法。但这些方法通常需独立、专用实验及不同模型和识别流程。本文提出一种统一的静态标定方法，仅通过一次简单实验即可识别包含几何与非几何效应（如柔顺弯曲、热变形、齿轮传动误差）的机器人模型。该模型在运动链中引入虚拟关节表征各误差源，并采用带解析梯度的高斯-牛顿优化实现参数识别。费舍尔信息谱显示估计良好条件化且参数化接近最小，系统性时间交叉验证与模型消融实验表明识别鲁棒。最终模型精度极高，对KUKA KR30机器人实现26.8μm平均位置误差，显著优于纯几何校准的102.3μm。
原文摘要 · Abstract (English)
Researchers have identified various sources of tool positioning errors for articulated industrial robots and have proposed dedicated compensation strategies. However, these typically require individual, specialized experiments with separate models and identification procedures. This article presents a unified approach to the static calibration of industrial robots that identifies a robot model, including geometric and non-geometric effects (compliant bending, thermal deformation, gear transmission errors), using only a single, straightforward experiment for data collection. The model augments the kinematic chain with virtual joints for each modeled effect and realizes the identification using Gauss-Newton optimization with analytic gradients. Fisher information spectra show that the estimation is well-conditioned and the parameterization near-minimal, whereas systematic temporal cross-validation and model ablations demonstrate robustness of the model identification. The resulting model is very accurate and its identification robust, achieving a mean position error of 26.8 $μm$ on a KUKA KR30 industrial robot compared to 102.3 $μm$ for purely geometric calibration.
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