A robot-soldering workstation combined with the deep learning and the template matching technology Online publication date: Mon, 26-Sep-2022
by Guoyang Wan; Tinghao Yi; Guofeng Wang; Kaisheng Xing; Yunsheng Fan
International Journal of Innovative Computing and Applications (IJICA), Vol. 13, No. 4, 2022
Abstract: To improve the stability and precision in PCB soldering application, an unmanned robot workstation is proposed to solve the problem of automatic soldering of different model chips. In the first step, a novel 2D hand-eye calibration method is developed to acquire a high-precision transformation model from the coordinate system of the vision system to the robot working coordinate system. Then a robust classification and location method is developed to realise high precision positioning of PCB objects, which is based on deep neural network combining with the traditional template matching. The proposed method solves the problems of poor positioning accuracy and low recognition rate when traditional machine vision detects PCB objects with the same shape but different models. And the automation of chip soldering is realised. The experimental results show that the proposed algorithm displays excellent robustness.
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