Online identification of time-varying dynamical systems for industrial robots based on sparse Bayesian learning  

作　　者：SHEN Tan DONG YunLong HE DingXin YUAN Ye 

机构地区：[1]School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China

出　　处：《Science China(Technological Sciences)》2022年第2期386-395,共10页中国科学（技术科学英文版）

基　　金：supported by the National Key R&D Program of China(Grant No.2018YFB1701202)。

摘　　要：Nowadays, industrial robots have been widely used in manufacturing, healthcare, packaging, and more. Choosing robots in these applications mainly attributes to their repeatability and precision. However, prolonged and loaded operations can deteriorate the accuracy and efficiency of industrial robots due to the unavoidable accumulated kinematical and dynamical errors. This paper resolves these aforementioned issues by proposing an online time-varying sparse Bayesian learning(SBL) method to identify dynamical systems of robots in real-time. The identification of dynamical systems for industrial robots is cast as a sparse linear regression problem. By constructing the dictionary matrix, the parameters of the robot dynamics are effectively estimated via a re-weighted1-minimization algorithm. Online recursive methods are integrated into SBL to achieve real-time system identification. By including sparsity and promoting online learning, the proposed method can handle time-varying dynamical systems and therefore improve operational stability and accuracy. Experimental results on both simulated and real selective compliance assembly robot arm(SCARA) robots have demonstrated the effectiveness of the proposed method for industrial robots.

关 键 词：industrial robots sparse Bayesian learning online identification 

分 类 号：TP242.2[自动化与计算机技术—检测技术与自动化装置]