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%。
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.
作者
苏成志
李玉春
刘森
侯爵
巴麒蛟
SU Chengzhi;LI Yuchun;LIU Sen;HOU Jue;BA Qijiao(School of Mechanical and Electrical Engineering,Changchun University of Science and Technology,Changchun 130022,China)
出处
《机械科学与技术》
北大核心
2025年第5期833-839,共7页
Mechanical Science and Technology for Aerospace Engineering
基金
国家基础科研计划(JCKY2019411B001)
吉林省科技发展计划(20210201041GX)。
关键词
工业机器人
标定
仿真数据驱动
预测
优选
industrial robot
calibration
simulation data-driven
prediction
optimization
