有鑒於文獻中常用來計算掃描組態的平整塊演算法(flat patch recursive algorithm)在掃描效率上的低落，本研究將影像分割常用的區域成長(Region Growing)概念，擴展於物體三維網格模型上計算出掃描組態，並且以七種物體模型進行模擬測試，之後整合結構光掃描系統、六軸機械手臂進行實際掃描並和文獻演算法進行比較，模擬及實驗結果顯示：本研究所提出的演算法隨著模型網格數增加，姿態數及耗費時間會漸漸少於文獻演算法，且整體涵蓋率相差不大，可有效縮短掃描時間並防止多視角點雲間定位誤差的累積。 The study integrates structure light system with six-axis manipulatorto examine the algorithm performance in different situation. Generally, based on the CAD model, flat patch recursive algorithm is used to calculate the scanning gestures for robot equipped with a 3D scanner. However, the required number of gestures calculated by flat patch recursive algorithm is significant for CAD model with huge number of meshes. To improve the efficiency of existing scanning gesture searching algorithm, this study expanded the concept of region growing which is commonly used in image segmentation to three-dimensional mesh model to calculate the potential scanning gestures. In this study, seven different objects with CAD models have been used to compare performance between the proposed algorithm and flat patch recursive algorithm. The experimental result shows that although the scanning area is a little bit less than the existing algorithm, the number of scanning gesture of proposed algorithm is significantly reduced and thus prevents accumulating error resulted from multiple view registration. In addition, the computation time of proposed algorithm is less than the one of flat patch recursive algorithm.