Robot 3D spatial motion measurement via vision-based method. (3rd March 2023)

• Record Type: Journal Article
• Authors: Wu, Lulu; Liu, Yi; Wang, Yuan; Du, Xin; Xiong, Xinyan; Jiang, Benchi
• Is Part Of: Measurement science & technology. Volume 34:Number 6(2023)
• Publication Date: 2023-03-03
• Subjects: robot spatial motion measurement -- binocular vision -- 3D coordinates reconstruction -- singular value decomposition; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/acbb95
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Measuring the motion of a robot accurately is an important and integral part of evaluating the dynamic and static performance of the robot. The performance index of a robot, such as kinematic accuracy, bearing capacity, deformation, vibration, stability, and structural mode can all be calculated according to the motion displacement of the robot. Therefore, improving the robot motion measurement method, promoting the measurement accuracy, and enriching the measurement content have received considerable scholarly attention worldwide. In this paper, an approach based on binocular vision was proposed to measure the 3D spatial motion of a robot. In the process of reconstructing robot movement, a mathematical model that can facilitate the solving process and improve the accuracy of results was derived to build 3D coordinates information. A novel coordinate transformation method that is based on the singular value decomposition was drawn up to realize the transformation from camera coordinates to robot coordinates. Several experiments were carried out on the self-built three-degree-of-freedom rectangular coordinate robot platform. The marker was designed specially and glued to the end of robot, and a new train of thought was adopted to extract the marker's feature point. The vision-based measurement results were compared with the actual coordinate value. Results of experiments demonstrated that the proposed method can successfully reconstruct the 3D spatial motion of a robot more exactly, which can meet the requirements of high-precision motion control, motion performance evaluation, and operation state evaluation.