Variable Stiffness Identification and Configuration Optimization of Industrial Robots for Machining Tasks

作     者：Jiachen Jiao Wei Tian Lin Zhang Bo Li Junshan Hu Yufei Li Dawei Li Jianlong Zhang Jiachen Jiao;Wei Tian;Lin Zhang;Bo Li;Junshan Hu;Yufei Li;Dawei Li;Jianlong Zhang

作者机构：Nanjing University of Aeronautics and AstronauticsNanjing 210016China Beijing Institute of Space Launch TechnologyBeijing 100076China Beijing Institute of Mechanical EquipmentBeijing 100854China 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2022年第35卷第5期

页      面：275-290页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 0811[工学-控制科学与工程] 080201[工学-机械制造及其自动化] 

基　　金：National Natural Science Foundation of China(Grant No.51875287) National Defense Basic Scientific Research Program of China(Grant No.JCKY2018605C002) Jiangsu Provincial Natural Science Foundation of China(Grant No.BK20190417). 

主　　题：Industrial robot Space gridding Variable stiffness identification Configuration optimization Smooth processing 

摘      要：Industrial robots are increasingly being used in machining tasks because of their high flexibility and intelligence.However,the low structural stiffness of a robot significantly affects its positional accuracy and the machining quality of its operation equipment.Studying robot stiffness characteristics and optimization methods is an effective method of improving the stiffness performance of a robot.Accordingly,aiming at the poor accuracy of stiffness modeling caused by approximating the stiffness of each joint as a constant,a variable stiffness identification method is proposed based on space gridding.Subsequently,a task-oriented axial stiffness evaluation index is proposed to quantitatively assess the stiffness performance in the machining direction.In addition,by analyzing the redundant kinematic characteristics of the robot machining system,a configuration optimization method is further developed to maximize the index.For numerous points or trajectory-processing tasks,a configuration smoothing strategy is proposed to rapidly acquire optimized configurations.Finally,experiments on a KR500 robot were conducted to verify the feasibility and validity of the proposed stiffness identification and configuration optimization methods.