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研究生： 楊子右
Tz-Yu Yang
論文名稱： 高負載自主移動機器人的定位及逆向運動學
Positioning and Inverse Kinematics of High-payload Autonomous Mobile Robot (HAMR)
指導教授： 林柏廷
Po-Ting Lin
口試委員: 林紀穎
Chi-Ying Lin
陳永耀
Yung-Yao Chen
馬劍清
Chien-Ching Ma
學位類別： 碩士
Master
系所名稱： 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年： 2023
畢業學年度： 111
語文別： 中文
論文頁數： 135
中文關鍵詞： 高負載自主移動機器人 、室內定位 、多點定位量測 、冗餘自由度機器人逆向運動學
外文關鍵詞： High-payload Autonomous Mobile Robot, Indoor Positioning, Multi-lateration, Inverse Kinematics of Redundant Degree of Freedom Robot
相關次數： 點閱：397  下載：11 
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    • 隨著智慧製造以及工業4.0的快速發展，製造、加工業及智慧物流業開始大量使用移動式機器人（Autonomous Mobile Robot, AMR）來進行產品的搬運及移動，在產品為高負載與高經濟價值的產業中，如：航太、軍事與精密製造產業，高負載自主移動式機器人（High-payload Autonomous Mobile Robot, HAMR）技術具有很大的開發價值，在上述產業中，為了能夠使HAMR有更佳的協作能力，HAMR之室內定位技術以及運動分析越發重要，成為了智慧製造研究的重點。目前多數的導航定位技術，利用相機或是使光學雷達（Light Detection And Ranging, LiDAR），來進行室內定位及導航的工作，其影像定位精度大約為30至40 mm，LiDAR的定位精度約為100 mm，為了能夠補足原先設備上的不足及降低開發成本，HAMR可以使用二次定位，來進行空間位置的雙重確認，以更精準地達成任務。
    於是本論文提出一種基於空間中設置的ArUco碼，進行多點定位量測技術的HAMR室內定位技術，其量測精度誤差為15 mm；於運動學分析方面，則透過差分進化演算法（Differential Evolution, DE），來解析逆向運動學，實現冗餘自由度之HAMR動態分析，以上技術將使HAMR能更精準的進行取物任務。

    The usage of autonomous mobile robots (AMRs) in manufacturing, processing, and intelligent logistics industries has increased significantly as a result of the quick growth of smart manufacturing and Industry 4.0. AMRs are used for both product handling and transportation. The development of High-payload Autonomous Mobile Robots (HAMRs) has enormous potential for use in industries with large payloads and high economic value products, such as aerospace, military, and precision manufacturing. In these industries, indoor positioning technology and motion analysis are crucial for HAMRs to achieve better coordination and collaboration.
    For indoor position and navigation, the majority of navigation and positioning systems now use cameras or optical Light Detection and Ranging (LiDAR) sensors, with LiDAR positioning precision hovering around 100 mm and image-based positioning accuracy ranging from 30 to 40 mm. To address the limitations of existing equipment and reduce development costs, HAMRs can employ dual-positioning using secondary localization methods for precise spatial confirmation and task execution.
    Therefore, this paper proposes an indoor positioning technology for HAMRs based on ArUco codes placed in the environment, enabling multi-lateration measurements with an accuracy error of 15 mm. In terms of motion analysis, the paper utilizes Differential Evolution (DE), an evolutionary algorithm, to solve inverse kinematics and achieve dynamic analysis of HAMRs with redundant degrees of freedom. These techniques will enhance the precision of HAMRs in performing pick-and-place tasks.

    摘要 i ABSTRACT ii 誌謝 iv 目錄 v 圖目錄 viii 表目錄 xii 符號索引 xiii 第一章 緒論 1 1.1 前言 1 1.2 動機及目的 2 1.3 論文架構 3 第二章 文獻回顧 5 2.1 自主移動機器人之發展 5 2.2 現有空間定位方法 7 2.2.1 里程定位 8 2.2.2 無線通訊定位 10 2.2.3 地圖特徵定位 12 2.3 機器人運動學 16 2.3.1 D-H參數法 16 2.3.2 六軸機械手臂順向運動學 18 2.3.3 六軸機械手臂逆向運動學 19 2.3.4 路面載具運動學 21 2.3.5 冗餘自由度機器人運動學 24 2.4 數位影像相關法量測 29 第三章 研究方法及工具 33 3.1 實驗流程 33 3.2 實驗工具 34 3.2.1 HAMR 35 3.2.2 KUKA機械手臂 36 3.2.3 三維相機 38 3.2.4 ArUco碼 40 3.3 DIC機械手臂位移精度量測 41 3.4 雷射干涉儀機械手臂精度驗證 42 3.5 空間座標設置 43 3.6 多點定位量測法 44 3.6.1 多點定位量測近似解 44 3.6.2 三點定位量測之解析解 48 3.7 機器人運動學 49 3.7.1 D-H參數表 49 3.7.2 KUKA六軸機械手臂順向運動學 51 3.7.3 9自由度HAMR運動學 52 第四章 實驗結果 60 4.1 機械手臂精度驗證 60 4.2 HAMR路面載具LiDAR導航精度 63 4.3 多點定位量測精度 65 4.3.1 多點定位量測數據震盪 65 4.3.2 多點定位量測ArUco碼辨識數量 68 4.3.3 多點定位量測對比實際空間量測誤差 69 4.3.4 DIC與多點定位量測誤差比較 72 4.4 DE演算法解逆向運動學 79 第五章 結論與未來展望 85 5.1 結論 85 5.2 未來展望 86 參考文獻 88 附錄A 95

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