Joint stiffness identification of industrial serial robots using 3D digital image correlation techniques

Abstract

Industrial robots have been widely used in manufacturing for advantages of flexibility and high efficiency, while there exists a critical problem of low stiffness. Measuring the stiffnesses of joints accurately have a positive effect on optimizing the stiffness through compensation or posture adjustment. This study proposed a new method for stiffness identification of serial industrial robots using 3D digital image correlation (3D-DIC) techniques, which exhibits high accuracies. External forces are applied to the robot end and its 6-dimensional displacements are recorded with a 3D-DIC system. The values of joint stiffness are evaluated from the data of robot configurations, displacements and forces. The proposed method is implemented on the KUKA KR600-2830 robot experimentally and the average absolute value of relative error is 5.8%, which demonstrates that the proposed method provides much improved accuracy compared to the traditional method.

A Deep Learning-Based Robot Analysis Model for Semantic Context Capturing by Using Predictive Models in Public Management

“…In the industrial sector (Lin et al, 2022), semantic analysis of robot images can help robots automatically identify and detect product defects, thereby improving production quality and efficiency. Moreover, it can be applied in intelligent warehousing (Dai et al, 2021), logistics , and other fields to achieve automated and intelligent logistics management.…”