A robot-soldering workstation combined with the deep learning and the template matching technology. (16th September 2022)

• Record Type: Journal Article
• Authors: Wan, Guoyang; Yi, Tinghao; Wang, Guofeng; Xing, Kaisheng; Fan, Yunsheng
• Is Part Of: International journal of innovative computing and applications. Volume 13:Number 4(2022)
• Page Start: 221
• Page End: 231
• Publication Date: 2022-09-16
• Subjects: deep learning -- template matching -- hand-eye calibration -- coordinate transformation -- object detection; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

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.