本論文提出一基於立體視覺之機器人3D物件拿取研究。其系統分成三大部分，分別為任意物體邊緣提取系統、夾取區域識別系統以及夾取姿態生成系統。任意物體邊緣提取系統以人類視覺感官判定物體型態來分類出物體不同類別，搭配背景摳圖技術，於任意背景環境下取出物體輪廓，並透過形態學影像處理提取物體完整輪廓。而在物體夾取區域識別使用前述獲得的物體完整輪廓並根據物體型態分類器類別不同來識別不同的夾取位置，生成物體平面夾取區域座標，再透過座標轉換關係將影像座標轉換至機器手臂座標來生成夾取姿態，最後將轉換後的結果輸入至於機器手臂上進行實際夾取動作。
本論文使用此一系統進行座標轉換定位實驗來驗證座標轉換之間的誤差關係，並利用物體碰點實驗分析夾取姿態誤差以及透過實際機器手臂進行物體夾取位置定位實驗，確定該物體是否可以實際轉換至手臂上進行夾取任務，透過搭配機械手臂進行完整操作可以佐證本論文，其實驗結果具有高度可靠性。

This paper proposes research on robot 3D object picking based on stereo vision. The system is divided into three parts: object grasping pose generation, grasping area identification, and object grasping pose generation. The object grasping pose generation uses human visual senses to determine the object's shape and classify different categories of objects. With the background matting technology, the object's outline is extracted in any background environment, and the complete outline of the object is extracted through morphological image processing. In recognition of the gripping object area, the complete outline of the object obtained above is used, different gripping positions are identified according to the different types of object shape classifiers, the coordinates of the gripping area on the object plane are generated, and then the image coordinates are converted to the machine through the coordinate transformation relationship. The arm coordinates are used to generate the gripping posture, and finally, the converted result is input to the robot arm for the actual gripping action.
This paper uses this system to carry out coordinate transformation and positioning experiments to verify the error relationship between coordinate transformations, and uses the object collision point experiment to analyze the grasping attitude error and the actual robot arm to carry out the object grasp background ng position positioning experiment to determine whether the object can be The actual transfer to the arm to perform the gripping task, through the complete operation with the robotic arm, can support this paper, and the experimental results are highly reliable.

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