近年來，由於世界人口老化、少子化的問題，導致勞動力不足且人力成本高漲，使需要大量人力的製造業、加工產業等受到嚴重衝擊，這讓自動化的議題逐漸受到重視。同時，由於產業上逐漸走向「少量多樣」的趨勢，使得產線的彈性應變能力比起以往更為關鍵。以往利用工業機器人協助生產，必須透過其教導器針對特定物體作產線的設定，且其效能、精準度非常仰賴人員的技術程度，其過程也非常耗時且大幅降低作業的彈性能力。為了解決上述問題，整合電腦視覺進入自動化產線是一個有效的解決方法。在生產線上，經常讓機械手臂擔任動作重複性高的生產位置，如執行物件取放的裝箱、擺盤等任務。在自動化取放系統的應用中，必須知道物件在輸送帶上或是裝箱內的擺放位置與狀態，並且將它們放置到期望的位置，因此攝影機擷取的物體影像與三維空間的關係，以及物體姿態與機械手臂夾取的方式則變為重要的議題。本論文針對基於視覺之工業用機械手臂物件夾取進行研究，所探討的主題包含自動化系統架設、基於視覺之物體空間資訊擷取以及機械手臂對物件的夾取姿態等。本論文主要是以眼對手之雙眼視覺系統架構，利用特徵點匹配、形狀辨識檢驗物體在空間中的狀態，並對機械手臂下達命令使其進行物件夾取。實驗結果顯示，本論文所開發之基於視覺機械手臂系統確實能完成全自動物件夾取工作。

In recent years, because of low birth rate and aging population, labor force has become insufficient and labor cost keeps rising up. The manufacturing industries that require a large amount of labor force are severely impacted by these problems. Thus, the topic related to automation has become more and more important. In the meantime, because more and more industries are following the trend of "small-volume large-variety production", the flexibility and adaptability of production lines have become much more important than before. In the past, industrial robots that are used for assisting production lines were controlled by teach pendants in order to perform a specific task. The performance and accuracy of industrial robots were highly dependent on the proficiency of technicians. Furthermore, it took a lot of time to set up a production line and it was lack of flexibility. In order to solve the problems mentioned above, the integration of computer vision in automatic production lines is an effective solution. In production lines, robotic arms are frequently used to perform repetitive tasks, such as pick-and-place tasks for packing and placing objects. In the application of the pick-and-place system, the position of objects on a conveyor belt or the locations for placing objects have to be known, in order to place them correctly. Therefore, the topics related to the relationship between object images and object 3D positions and the relationship between object poses and object grasping methods are extremely important. This thesis focuses on the research topic of vision based object grasping of industrial manipulators. This research topic contains object spatial information, object grasping poses, and the eye-hand coordination and frame transformation. This thesis mainly uses the eye-to-hand stereo camera system to retrieve object’s 3D position information by feature matching, and uses shape recognition to give instructions to a robotic arm for picking and placing objects. Experimental results indicate that the vision based automatic system developed in this thesis can successfully complete automatic pick-and-place tasks.

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