Optimizing Simulation Data-driven Robot Kinematic Parameter Scaling Pose Sets

苏成志; 李玉春; 刘森; 侯爵; 巴麒蛟

doi:10.13433/j.cnki.1003-8728.20230233

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Optimizing Simulation Data-driven Robot Kinematic Parameter Scaling Pose Sets

更新时间：2026-04-23

- Optimizing Simulation Data-driven Robot Kinematic Parameter Scaling Pose Sets
- Mechanical Science and Technology for Aerospace Engineering Vol. 44, Issue 5, Pages: 833-839(2025)
- 作者机构：
  
  长春理工大学机构机电工程学院,长春,130022
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20230233
  CLC：
- Published：2025
- 稿件说明：
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苏成志, 李玉春, 刘森, et al. Optimizing Simulation Data-driven Robot Kinematic Parameter Scaling Pose Sets[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(5): 833-839.
DOI：

苏成志, 李玉春, 刘森, et al. Optimizing Simulation Data-driven Robot Kinematic Parameter Scaling Pose Sets[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(5): 833-839. DOI： 10.13433/j.cnki.1003-8728.20230233.

摘要

针对工业机器人标定精度受位姿集影响这一现象，提出了一种仿真数据驱动的位姿集优选方法。首先，通过标定以及离线补偿的方式获得机器人虚拟模型，在仿真空间中测量和标定构建位姿集与标定精度组成的数据集。其次，对数据集的特征进行提取，利用灰度关联算法计算特征因子与辨识精度之间的相关度，验证特征因子的准确性，降低数据维度，应用支持向量回归（Support vector regression

SVR）预测模型进行训练得到特征因子与辨识精度之间的映射模型。最后，利用SVR预测模型对机器人标定时采集的多组位姿集进行标定精度预测，优选出标定精度高的位姿集用以提高标定精度。实验结果证明，利用仿真数据驱动的方法优选位姿集比随机选择的位姿集对应的平均标定精度提高了22.9%。

Abstract

Because the calibration accuracy of an industrial robot is affected by its pose-set

a simulation data-driven pose-set optimization method is proposed. Firstly

the virtual model of the industrial robot is obtained by calibration and offline compensation

and the data set consisting of the pose-set and calibration accuracy is measured and calibrated in the simulation space. Secondly

the features of the data set are extracted

the correlation between the feature factors and the recognition accuracy is calculated by using the grayscale correlation algorithm

the accuracy of the feature factors is verified

the dimensionality of the data is reduced. Finally

the SVR (support vector regression) prediction model is applied to predict the calibration accuracy of multiple pose-sets collected during robot calibration

and the poses with high calibration accuracy are selected to improve the calibration accuracy. The experimental results demonstrate that the use of the simulation data-driven method improves the average calibration accuracy by 22.9% in selecting preferred pose-sets than randomly selected pose-sets.

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