Variable Parameters Stiffness Identification and Modeling for Positional Compensation of Industrial Robots

Abstract

Abstract Due to the weak stiffness of robot structure, the positional accuracy of industrial robots under load can hardly meet the application requirements of high-precision machining. Predicting and compensating errors by accurate stiffness modeling is an effective method to improve robot positional accuracy. Existing stiffness modeling methods use theoretical kinematic parameters and approximate the joint stiffness to a fixed value, so that the modeling accuracy is poor. Thus, this paper proposes a regular sampling point selection method by space gridding. Then, combining Levenberg-Marquardt kinematics parameter calibration and static joint stiffness identification methods, a comprehensive identification method is proposed to achieve simultaneous identifying of robot kinematics and stiffness parameters. Next, a variable parameter stiffness model could be established, according to the identification results in different workspaces. Finally, a model-based error prediction and compensation method is put forward through online sensing of external load. The error compensation is performed on a KR500 robot, and experimental results verified that the average value of absolute positional errors caused by external load, could be reduced by 44.61%, compared with the traditional compensation method.