本論文目的為設計一四自由度之機械手臂運動控制器，其結構用以模
擬人類由肩膀到手肘之間關節運動特性，此四關節之角度變化可達成手腕
之三維空間運動。由於此機械手臂以四自由度達成手臂末端點（即手腕）
之三維空間運動，因此其具贅餘自由度之問題。傳統上，可使用賈可賓矩
陣規劃具贅餘自由度之空間機械手臂運動問題。但於人類手臂在不同動作
情境下（如寫字、揮手、握手等等）會有不同之運動特徵，而賈可賓矩陣
並無法考慮到不同行為模式下之運動特徵，致使此機械手臂無法達到相似
於人類手臂在特定動作情境下之動作特性。因此本研究提出一以類神經網
路為基礎之手臂運動控制器，其藉由歸納出人類手臂在不同動作情境下之
運動特徵，其以不同模式之動作特徵產生一手臂逆向運動學之輔助解，以
使具贅餘自由度之逆向運動學得以求解，並同時使機械手臂可以達到仿人
手臂在特定動作情境下之運動行為。在系統實現上，本研究使用系統晶片
基礎之機械手臂控制器開發，其採用軟硬體協同設計架構，配置軟體與硬
體核心，以達到高效能之即時運算與控制能力。最後，本文以動作擷取系
統來驗證本機械手臂針對不同測試軌跡以及運動模式下之效能，其結果並
與賈可賓方法比較，來驗證此一類神經網路控制之可行性。

This study aims to develop a four degree-of-freedom (DOF) robotic manipulator
which is constructed to emulate the upper limb structure of human beings. Four joint motors are designed to perform the motions of a 3-DOF shoulder joint and a 1-DOF elbow joint. The 3D spatial motions of the end-effector (i.e., wrist) can be desired in terms of controlling the joint motor angles of the proposed 4-DOF robotic manipulator; hence, such a configuration results in the redundancy problem. In general, Jacobian solutions are linear approximations of inverse kinematics problems with redundancy conditions. Form the viewpoints of upper limbs’ motions of human beings, limb motions may be characterized as different motion scenarios. The same wrist position can be generated from different limb postures, and these postures depend on different motion scenarios such as writing words, waving hands, shaking hands, etc. As a
consequence, Jacobian solutions are difficult to realized specific limb motion
scenarios of human beings. Therefore, this thesis proposes a supervised neural
network based robotic manipulator control system which constructs limb motion
characteristic models according to relative joint posture features with respect to different motion scenarios. The generated motion features are further used to provide an auxiliary condition for eliminating the redundancy problem of the inverse kinematics as well as to meet specific motion scenarios. The proposed control system is implemented based on the “System on a Programmable Chip (SoPC)” techniques, and the proposed system is developed based on hardware-software co-design approaches. By properly allocating hardware and software modules, the system performance can be improved. Finally, several trajectory tracking experiments are done in terms of the Jacobian and neural network approaches, respectively. In order to
verify the system performance, this study employs a motion capture system to record the experiment results. Experiment results successfully demonstrated that the proposed neural network based control system performs similar motion behaviors when compared to Jacobian approaches for the same test trajectory and motion scenario.

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