近年來，自動化產業發展日漸成熟，人們對於自動化的要求越來越高。而突如其來之COVID-19新冠肺炎大流行，更導致人力吃緊，使得全自動化的需求越來越大。因此在工業界，攝影機已逐漸取代人眼成為基於視覺之控制應用中之視覺輔助工具。而在結合了電腦視覺與機械手臂之視覺伺服應用中，如何能夠規劃適當之影像特徵空間命令軌跡，使得機械手臂能在影像空間中進行精確的循跡或定位控制任務，更是重要且值得進一步深入研究之議題。因此在本論文中，實現一種近年來所提出之不同的特徵點選取方式來完成軌跡規劃，旨在於解決傳統基於影像之視覺伺服架構的缺點如：只能在目標姿態附近的區域確保基於影像之視覺伺服架構的穩定性；在眼在手的攝影組態中，所選取的特徵點可能會離開攝影機的視野等等。另外本論文也使用最佳化演算法來求得最佳化的速度命令。而透過將機械手臂末端效應器之速度矩中的旋轉速度與平移速度解耦，以解決計算時間過久的問題，最後達成基於影像之視覺伺服軌跡規劃任務。

In recent years, as the development of the automation industry has become more mature, expectations have been heightened accordingly. Moreover, the COVID-19 pandemic has also led to reduced access to manpower, adding to extant demands on full automation. Therefore, cameras have gradually replaced human vision as a visual aid tools for vision based control applications in industry. In visual servoing applications that combine computer vision and robot manipulators, one worthwhile avenue of research is an investigation into exactly how to plan a trajectory by choosing the appropriate image feature points in the image space for the robot manipulator to move correctly. Therefore, this thesis implements an alternative feature point selection method that has been developed in recent years in trying to overcome the drawbacks of traditional Image-Based Visual Servoing (IBVS) such as the stability of IBVS can only be ensured in the area near the target object; in eye-in-hand camera configuration, the selected feature points may leave the field-of-view (FOV), etc. In addition, it also uses an optimization algorithm to compute an optimized velocity profile. By decoupling the translation velocity and the orientation velocity in the robot manipulator end-effector’s velocity screw, computation time can be considerably decreased. In the end, trajectory planning for IBVS structures can be achieved.

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