在自動化生產線中，使用機械手臂進行零件分類為主要工作之一，在機械手臂拾取零件前，可透過3D深度學習快速辨識零件種類及數量。然而，當零件堆疊擺放於工作平台上時，難以將零件分割並分別進行辨識，且收集堆疊零件之數據非常耗費時間及人力，為克服以上問題，本論文以點資料處理自動化生成大量堆疊零件之數據，改善數據準備耗時及堆疊零件無法辨識之問題。
本論文透過點資料處理演算法及點雲匹配技術，將零件之點雲以隨機擺放方位進行模擬堆疊，並移除遮蔽點以模擬實際掃描情況，根據此方法生成大量的點雲數據，並自動化將點雲以點為單位新增種類標籤；將生成之數據輸入至PointNet深度學習模型進行訓練，訓練完成之模型平均準確率約為98.1%，而使用該模型辨識真實掃描點雲時，其準確率約為90.24%，兩者之誤差主要出現於零件之間的交界處。
本論文詳述如何使用零件之點雲進行模擬堆疊、3D深度學習之模型及訓練流程，並簡述如何搭配結構光掃描器與機械手臂進行隨機物件夾取，最後對實例驗證之結果進行探討。

In an automated production line, using manipulators to sort parts is one of the main tasks. Before picking up the parts, 3D deep learning can be used to recognize the type and quantity of the parts quickly. However, when the parts are stacked and placed on a work platform, it is difficult to recognize each individual part. The collection of point clouds for all possible combinations of the stacked parts is also very time-consuming and labor-intensive. This thesis approaches the subject by using point data processing to generate a large number of stacked parts automatically, so as to improve the time-consuming of data preparation and unrecognizable problems of stacked parts.
In this thesis, through the point data processing algorithm, the point cloud of the parts is stacked in a random orientation, and the hidden points are removed to simulate the actual scanning situation. According to this method, a large abundant amount of point cloud data is generated and input to PointNet model for deep learning. The average accuracy of the trained model in the test dataset is about 98.1%, and the accuracy in real scanned data is about 90.24%.
In additional to discussing the generation process of stacked point clouds and the training process of 3D deep learning, this thesis also describes the integration of a structured light scanner and a manipulator for gripping and sorting of complex parts, and finally, discusses the results of some case studies.

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