摘要
针对串联型工业机器人的绝对定位误差较大的问题,该文提出一种两级误差标定方法,该方法融合了误差模型法和基于径向基(radial basis function,RBF)神经网络的非模型标定方法。首先,基于M-DH(modified DH)运动学模型建立串联型工业机器人的位姿误差模型,并基于差分进化(differential evolution,DE)优化算法实现M-DH运动学参数误差的辨识,将TX60机器人的平均综合位置/姿态误差从(0.5368 mm,0.1745°)降低为(0.1772 mm,0.0875°)。其次,为了进一步提升机器人的精度性能,利用RBF神经网络拟合预测TX60机器人的剩余误差,该方法将机器人的平均综合位置/姿态误差从(0.2178 mm,0.0863°)降低为(0.1044 mm,0.0411°)。最后,通过实验验证基于两级误差标定方法的精度提升效果要优于单一的基于RBF神经网络的误差标定方法,平均综合位置/姿态误差降低比例分别是4.9%和14.9%。因此,该文提出的两级误差标定方法能够有效地提升机器人的精度性能。
Aiming at the problem of large absolute positioning error of serial industrial robots,this paper presents a double-stage error calibration method which combines the error model based method and the nonmodel calibration method based on RBF neural network.Firstly,the pose error model of the serial industrial robot is established based on the M-DH kinematics model.The errors of M-DH kinematic parameter are identified based on the DE optimization algorithm.The average comprehensive position/attitude errors of TX60 robot are reduced from(0.5368 mm,0.1745°)to(0.1772 mm,0.0875°).Secondly,the RBF neural network is used to fit and predict the residual error which can further improve the accuracy performance of TX60 robot.This method reduces the average comprehensive position/attitude errors of TX60 robot from(0.2178 mm,0.0863°)to(0.1044 mm,0.0411°).Finally,the experiments have been done to verify the accuracy improvement effect based on the double-stage error calibration method is better than the single-stage error calibration method based on the RBF neural network.The average comprehensive position/attitude errors respectively decrease by 4.9%and 14.9%.The experimental results prove that the double-stage error calibration method proposed in this paper can effectively improve the accuracy performance of robot.
作者
乔贵方
田荣佳
张颖
王保升
宋光明
宋爱国
QIAO Guifang;TIAN Rongjia;ZHANG Ying;WANG Baosheng;SONG Guangming;SONG Aiguo(Automation Department,Nanjing Institute of Technology,Nanjing 211167,China;School of Instrument Science and Engineering,Southeast University,Nanjing 210096,China;Research Department of Intelligent Manufacturing Equipment,Nanjing Institute of Technology,Nanjing 211167,China)
出处
《中国测试》
CAS
北大核心
2022年第7期134-139,146,共7页
China Measurement & Test
基金
国家自然科学基金项目(5190525)
中国博士后科学基金(2019M650095)
江苏省高等学校自然科学研究面上项目(17KJD460006)。
