基于改进指数优化与迭代加权LM法的机器人标定方法  

Robot Calibration Based on Improved Exponential Optimization with Iterative Weighted LM Method

作　　者：赵云涛 方成 李维刚[1,2] ZHAO Yuntao;FANG Cheng;LI Weigang(College of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430081,China;Engineering Research Center of Metallurgical Automation and Measurement Technology of Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,China)

机构地区：[1]武汉科技大学信息科学与工程学院,武汉430081 [2]武汉科技大学冶金自动化与检测技术教育部工程研究中心,武汉430081

出　　处：《组合机床与自动化加工技术》2024年第11期18-23,共6页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：湖北省教育厅科学技术研究项目(B2020012);武汉都市圈协同创新科技项目(2024070904020439)。

摘　　要：针对协作机器人的出厂标定过程步骤繁琐,效率低下的问题,提出了一种基于两步策略的运动学标定算法,通过结合改进指数优化算法和迭代加权LM算法,简化标定流程并提高定位精度。首先,基于改进DH方法与位置微分误差变换建立机器人参数辨识模型,结合测量装置坐标系搭建标定系统误差模型,利用改进指数优化算法快速获取测量坐标系初始参数;其次,为提高辨识结果的鲁棒性,将距离残差作为权值因子,通过迭代加权LM算法补偿机器人模型的几何参数误差和测量坐标系矩阵参数误差;最后,通过实验验证,结果表明机器人位置误差的平均值、均方差误差和最大误差分别降低了80.28%、71.61%和52.16%,验证了该方法的正确性和有效性。Aiming at the cumbersome and inefficient factory calibration process of collaborative robots,this paper proposes a kinematic calibration algorithm based on a two-step strategy,which combines the improved exponential optimization algorithm with the iterative weighted LM algorithm to simplify the calibration process and improve the positioning accuracy.First,a robot parameter identification model is established based on the improved DH method and position differential error transformation,and a calibration system error model is built by combining the coordinate system of the measurement device.The improved exponential optimization algorithm is used to quickly obtain the initial parameters of the measurement coordinate system.Secondly,in order to improve the robustness of the identification results,the distance residual is used as a weighting factor,and the geometric parameter error of the robot model and the matrix parameter error of the measurement coordinate system are compensated by the iterative weighted LM algorithm.Finally,through experimental validation,the results show that the mean squared error and maximum error of the robot position error are reduced by 80.28%,71.61%and 52.16%,respectively,which verifies the correctness and effectiveness of the method.

关 键 词：协作机器人 运动学标定 指数优化算法 LM算法 激光跟踪仪 

分 类 号：TH165[机械工程—机械制造及自动化] TG659[金属学及工艺—金属切削加工及机床]