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基于改进鸡群优化算法运动学与改进鸡群优化-Elman神经网络非运动学的机器人误差标定

Robot Error Calibration Based on Improved CSO Algorithm Kinematics and Improved CSO-Elman Neural Network Non-kinematics
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摘要 针对工业机器人末端定位误差标定问题,本文从机器人运动学与非运动学两个方面对机器人进行定位误差标定。针对机器人运动学方面,建立了运动学误差模型并提出一种改进CSO(chicken swarm optimization)算法对机器人进行几何参数误差辨识,通过对比LM(Levenberg-Marquardt)迭代算法与PSO(particle swarm optimization)算法验证本文所提出算法的效果,以IRB1200机器人为实验对象,通过APIT3激光跟踪仪采集误差数据,搭建了机器人误差标定实验平台进行实验。经实验测量,机器人末端平均定位误差由2.76 mm下降至1.45 mm,提升了47.5%;同时针对机器人非运动学方面,使用在运动学误差标定中提出的改进CSO算法优化了Elman神经网络的初始阈值与权重,并使用初始参数优化的Elman神经网络建立机器人末端位置误差与机器人关节角之间的映射关系,以预测训练完成的机器人立方体空间内的机器人位置误差,并对比了普通Elman神经网络的预测效果。经实验测量,机器人末端平均定位精度较标定前提升34.9%,验证了本文所提出神经网络的拟合预测效果。 Aiming at the positioning error calibration problem of industrial robots,we combine the kinematics and non-kinematics aspects to calibrate the positioning error of robots.Aiming at the kinematics of a robot,a kinematics error model is developed and an improved chicken swarm optimication(CSO)algorithm is proposed to identify the geometric parameter error of the robot.The effect of the proposed algorithm is verified by comparing the Levenberg-Marquardt iterative algorithm and the particle swarm optimization algorithm.The IRB1200 robot is taken as the experimental object,and error data are collected using an APIT3 laser tracker.A robot error calibration experiment platform is built to conduct experiments.The experimental measurement shows that the average positioning error of the robot end is decreased from 2.76 mm to 1.45 mm,which is increased by 47.5%.Furthermore,for the non-kinematic aspect of the robot,the improved CSO algorithm proposed in the kinematic error calibration is used to optimize the initial threshold and weight of the Elman neural network,and the Elman neural network optimized with initial parameters is used to establish the mapping relationship between the robot end position error and the robot joint angle to predict the robot position error in a trained robot cube space.The prediction effect of the ordinary Elman neural network was compared.The experimental measurement shows that the average positioning of the robot end is improved by 34.9%compared with that before calibration,which verifies the fitting prediction effect of the neural network proposed in this study.
作者 江小辉 孙翼飞 郭维诚 侯春杰 任斐 JIANG Xiaohui;SUN Yifei;GUO Weicheng;HOU Chunjie;REN Fei(School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Shanghai Aerospace Equipment Manufacturer Co.,Ltd.,Shanghai 200240,China)
出处 《信息与控制》 CSCD 北大核心 2024年第3期315-328,共14页 Information and Control
基金 上海市科学技术委员会项目(2051105704)
关键词 工业机器人 参数辨识 ELMAN神经网络 鸡群优化算法 industrial robot parameter identification Elman neural network chicken swarm optimization
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