臺灣博碩士論文加值系統

機械手臂在進行加工時，常因軸關節多餘(Redundance)的作動，失去了該有的工作效率，而減少產品的產出。且在加工過程中亦容易有奇異點問題的發生，使其失去一個操作自由度，進而降低操作性能。因此，為了增加加工效率，避免發生奇異點現象，本研究利用操作性作為奇異點量測參考值，結合軸關節平滑性，再利用多目標遺傳基因演算法( Multiple Objective Genetic Algorithms , MOGAs )求解加工路徑軸關節空間之最佳逆向運動解與最佳加工位置。
本論文主要是針對兩種不同的工業機械手臂，利用Denavit Hertenberg所提出的方法，求解機械手臂尖端執行器與基座之空間座標系，並在不失軸關節最小變動量下，搜尋機械手臂最佳加工環境位置與最佳操作性。最後，為了呈現實驗所達到之成果及避免錯誤的產生，實驗中將會透過CATIA軟體進行實體機械手臂動作模擬。

The efficiency of robotic arm manipulators is greatly reduced due to the extra moves made by axle joints, and will eventually leads to underproduction. Also, the flexibility and manipulability are inevitably decreased during processing because of the emergence of singularity point. Therefore, in order to increase the processing efficiency and avoid the singularity point phenomenon, exploring the best inverse kinematics solutions of axle joints is the primary goal of this research. Measurements of singularity points based on the performance of manipulators, smoothness of axle joints and multiple objective genetic algorithms are applied to achieve the goal.
Two different industrial robotic arm manipulators are targeted in this research. The ultimate robotic arm manipulators positioning and performance are researched on condition that the least variation quantity has to be achieved. To do so, formulations suggested by Denavit Hertenberg are keys to explore space coordinate system of manipulator base and manipulator tip. Finally, simulations will be conducted by utilizing CATIA throughout the whole experiment to ensure that the outcome of the experiment will not be jeopardized by miscalculations.

中文摘要
ABSTRACT
誌謝
目錄
表目錄
圖目錄
第一章 緒論
1.1 前言
1.2 研究動機和目的
1.3 文獻回顧
1.4 本文架構
第二章 機械手臂之基礎理論
2.1 座標轉換基礎概念
2.2 齊次變換矩陣
2.3 D-H座標變換法
2.4 機械手臂微量的變動與JACOBIAN矩陣的變換
2.4.1 機械手臂微量運動的介紹
2.4.2 Jacobian矩陣的定義
2.4.3 Jacobian矩陣演繹法
第三章 遺傳基因演算法
3.1 發展與研究
3.1.1 初始染色體的產生
3.1.2 染色體之選擇和複製
3.1.3 基因交叉
3.1.4 基因突變
3.2 小生境的發展與研究
3.3 搜尋終止條件
第四章 應用小生境遺傳基因演算法於機械手臂軸關節參數最佳化
4.1 目標函數的定義
4.2 小生境遺傳基因演算法的流程
4.2.1 染色體之編碼
4.2.2 適應值之設計及計算
4.2.3 染色體之選擇
4.2.4 染色體之交叉
4.2.5 染色體之突變
4.2.6 小生境之演化步驟：
4.3 以六軸PUMA 560機械手臂為例
4.3.1 實驗結果與比較
4.4 以五軸RV_E3JM機械手臂為例
4.4.1 實驗結果與比較
第五章 機械手臂加工位置與軸關節之最佳化
5.1 多目標最佳化概念
5.2 多目標遺傳基因演算法之流程
5.3 多目標基因演算法之步驟
5.3.1 定義目標函數
5.3.2 基因編碼的設計
5.3.3 適應值的評估與分配
5.3.4 染色體之選擇、交叉與突變
5.4 以六軸PUMA 560機械手臂為例
5.4.1 PUMA 560 Jacobain的推導
5.4.2 實驗結果
5.5 以五軸RV_E3JM機械手臂為例
5.5.1 RV_E3JM Jacobain的推導
5.5.2 實驗結果
第六章 結論與未來展望
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