机械科学与技术2025，Vol.44Issue(5)：833-839,7.DOI:10.13433/j.cnki.1003-8728.20230233
仿真数据驱动的机器人运动学参数标定位姿集优选
Optimizing Simulation Data-driven Robot Kinematic Parameter Scaling Pose Sets
摘要
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.关键词
工业机器人/标定/仿真数据驱动/预测/优选Key words
industrial robot/calibration/simulation data-driven/prediction/optimization分类
信息技术与安全科学引用本文复制引用
苏成志,李玉春,刘森,侯爵,巴麒蛟..仿真数据驱动的机器人运动学参数标定位姿集优选[J].机械科学与技术,2025,44(5):833-839,7.基金项目
国家基础科研计划(JCKY2019411B001)与吉林省科技发展计划(20210201041GX) （JCKY2019411B001）
