未來社會人力資源短缺，許多產線面臨人力成本上升；而工業4.0的來臨也大大增加客製化訂單需求，使得傳統產線的單一功能性機械手臂不敷使用。另一方面，在需要進階人類技術的工作上，一般的末端效應器夾爪自由度少，無法做到較為複雜之工作，因此在此項革新的概念下，智慧機械的開發逐漸熱絡，除了產線上的資料蒐集、處理之外，高自由度的機械手臂末端效應器開發則扮演著相當重要的角色，本研究希望開發出一套具有高自由度之類人形機械手，以替未來的各項機器人需求做為根基。
在開發硬體設備之餘，要如何控制如此高自由度之夾爪也是相當重要的課題之一。本研究中針對所開發出的夾爪進行正向以及逆向運動學，旨在找出各軸關節角度與後端輸出之指尖座標關係，以做到精準運動命令控制。此種高自由度的冗餘機器人在運動分析上會遇到最佳化的問題，本研究中提出以基因演算法作為多指逆向運動學之最佳化演算法，以解決冗餘且高度耦合之非線性最佳化逆向運動學。

There is a shortage of human resources in the future, and many production lines are facing rising labor costs. The advent of Industry 4.0 has also greatly increased the demand for customized orders, making the single functional robotic arm of the traditional production line inadequate. On the other hand, in the work that requires advanced human technology, the general gripper of the end effector have less freedom and cannot do more complicated work. Therefore, under the concept of this innovation, the development of smart machinery is gradually warming up. In addition to data collection and processing on the production line, the development of high-degree-of-freedom robotic end effectors plays a very important role. This research hopes to develop a humanoid gripper with a high degree of freedom to serve as the foundation for future robotic needs.
In addition to developing hardware devices, how to control such high-degree-of-freedom jaws is also one of the most important topics. In this study, the forward and inverse kinematics of the proposed gripper are designed to find the relationship between the angle of each joint and the fingertip, so as to achieve precise motion command control. Such high-degree-of-freedom redundant robots will encounter optimization problems in motion analysis. In this study, the genetic algorithm is proposed as the optimization algorithm for multi-finger inverse kinematics to solve redundant and high coupling nonlinear optimization of inverse kinematics.

論文摘要 I
Abstract II
致謝 III
第一章 緒論 1
1.1 前言 1
1.2 文獻回顧 2
1.3 研究動機 6
1.4 研究方法 7
第二章 理論背景 8
2.1 前言 8
2.2 手部生物力學 9
2.3 Denavit-Hartenberg參數 11
2.4 正、逆向運動學 13
2.5 基因演算法 17
第三章 仿生機械手設計 24
3.1 手部關節機構 24
3.1.1 單自由度手指關節 24
3.1.2 雙自由度手指關節 26
3.1.3 手腕連桿關節 30
3.2 驅動系統 33
3.3 嵌入式系統設計 37
第四章 運動學分析 43
4.1 手部關節建模 43
4.2 正向運動學 45
4.2.1 關節角度與馬達輸入線性度 45
4.2.2 工作空間分析 48
4.3 逆向運動學 53
4.3.1 單指逆向運動學 53
4.3.2 使用基因演算法解多指逆向運動學 58
4.3.3 結合工作空間運算基因演算法 64
4.3.4 基因演算法流程圖 67
第五章 基於基因演算法之逆向運動學模擬 70
5.1 問題定義與基因演算法設定 70
5.2 基因演算法參數討論及表現 73
5.3 基於工作空間前饋之收斂速度表現 77
第六章 結論與未來工作 81
6.1 總結 81
6.2 本文貢獻 81
6.3 未來展望 82
參考文獻 84

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