少量多樣的電子連接器產品製造和裝配需要大量的人力資源，但廠商面臨嚴重的缺工問題。因此，本研究開發一套取放料系統，使用六軸機械手臂進行膠體零件散料夾取，此系統結合機械手臂、光源控制、機器視覺以及深度學習等技術，分別使用兩種不同作業系統來實現系統的開發，使用 Windows 作業系統來完成環狀光源與控制器的整合，且基於 Linux Ubuntu18.04 版本的作業系統環境，透過機器人作業系統(Robot Operating System,ROS)開發軟體系統，經由 ROS 分散式的架構將實驗用電腦、六軸機械手臂、工業相機及自適應夾爪與深度學習、機器視覺進行軟硬體的整合，以達到散料辨識夾取系統的開發。 本論文首先透過基因演算法於調整光源參數，進而增強 YOLOv4 模型辨識膠體正反面的效果，並有效地解決了由於環境因素導致辨識率降低的問題，接著使用六軸機械手臂，將散料膠體依照其正反面準確的夾取至指定的治具上，目前此系統的成功率為94%，成果顯示本論文能夠成功克服原先合作廠商須採用在少量多樣或試產時尚未投入成本建置振動盤進行料件取料問題。;Manufacturing and assembling a variety of electronic connector products in small quantities require a significant amount of human resources, yet manufacturers are grappling with severe labor shortages. Hence, this research presents the development of an electronic connector materials handling system that employs a six-axis robotic arm for the precise retrieval of adhesive components. This study utilized two different operating systems for system development. Windows operating system was employed for integrating the light source and controller, while the Linux Ubuntu 18.04 version operating system environment was used to develop the software system through the Robot Operating System (ROS). The computer, six axis robot arm, industrial camera adaptive gripper, and deep learning and machine vision technologies were integrated by ROS distributed architecture, ultimately achieving the development of a system for picking micro connector component. This research employs genetic algorithm to optimize the parameters of the light source, followed by utilizing the YOLOv4 deep learning model to identify the front sides or back sides of the plastic component. Then use six-axis robotic arm to handle these materials with precision, placing them onto designated fixtures. Currently, this system boasts a success rate of 94%. The results highlight the successful resolution of the challenge faced by collaborating manufacturers, eliminating the need to invest in vibration feeders for scenarios involving small scale, diverse, or trial production.
