在高度自動化發展的現狀下，機械手已經廣泛應用於各個領域。對軟機械手的研究越來越受歡迎。然而，柔性機械手的運動學比一般機械臂複雜得多。因此，難以設計和控制。簡化具有多個自由度的連續體機器人的製造。這項研究提出了一個連續運動模塊（CKM）。單個 CKM 主要由連接五塊板的四個萬向節、支撐的壓縮彈簧和三根獨立的拉索組成。每個 CKM 的底部可以與相鄰的 CKM 或機器人平台連接。在控制方面，所有驅動電纜均由遠端位置的線性致動器控制。為了證明所提出的 CKM 的功能和可行性，製作了一個連續體機器人。連續體機器人主要由三個可移動的 CKM 和兩個定長的延伸部分組成。控制主要是通過一個驅動電纜模具。團體。驅動電纜模塊主要由九個線性滑台組成，用作 CKM 的控制線性執行器。並在機器人底部安裝移動滑台，使連續體機器人執行基於雅可比的圖像伺服跟踪任務，實現向前移動的功能。連續體機器人執行的基於雅可比的圖像伺服跟踪任務包括使用眼手 (ETH) 配置跟踪心形軌跡以及插入/拔出具有手眼(EIH) 配置的電源適配器。在圖像跟踪點的提取中，利用 YOLO-V3 對象識別獲取目標的主要位置，通過圖像處理提取目標的特徵點作為跟踪點。最後，通過定量跟踪誤差分析來評估整體性能。

The research on soft manipulators is becoming more and more popular. However,
the kinematics of flexible manipulators is much more complicated than general arms.
Therefore, it is difficult to design and control. To ease the fabrication of a continuum robot with multiple degrees of freedom. This study proposed a continuum motion module (CKM). A single CKM is mainly composed of four universal joints connecting five plates, compression springs for support, and three independent tension cables. The bottom of each CKM can be connected with the neighboring CKM or robot platform. All tension cables are controlled by linear actuators at a distal site. In order to demonstrate the function and feasibility of the proposed CKM, a continuum robot was made. The continuum robot is mainly composed of three movable CKMs and two fixedlength extensions. The control is mainly through a drive cable mold. group. The drive cable module is mainly composed of nine linear sliding tables, which are used as the control linear actuators of CKM. And a moving sliding table is installed at the bottom of the robot to enable the continuum robot to perform the Jacobian-based image servo tracking task to achieve the function of moving forward. The Jacobian-based image servo tracking tasks performed by the continuum robot include tracking a heart shape trajectory with eye-to-hand (ETH) configuration and plugging/ unplugging a power adapter with eye-in-hand (EIH) configuration. In the extraction of image tracking points, the YOLO-V3 object recognition is used to obtain the main position of the target, and the feature points of the target are extracted as the tracking point through image processing. Finally, the overall performance is evaluated through quantitative tracking error analysis

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