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研究生: 邱軒博
Chiu, Hsuan-Po
論文名稱: 全向移動平台結合機械手臂動態物件追蹤
Omni-directional Mobile Platform combined with Robotic Arm Dynamic Object Tracking
指導教授: 呂藝光
Leu, Yih-Guang
口試委員: 呂藝光
Leu, Yih-Guang
吳政郎
Wu, Jenq-Lang
陶金旺
Tao, Chin-Wang
張原彰
Chang, Yuan-Chang
陳松雄
Chen, Song-Shyong
口試日期: 2024/07/30
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2024
畢業學年度: 112
語文別: 中文
論文頁數: 74
中文關鍵詞: 全向移動平台機械手臂雙目測距物件追蹤
英文關鍵詞: Omnidirectional mobile, Robotic Arm, Binocular ranging, Object Tracking
研究方法: 實驗設計法行動研究法比較研究
DOI URL: http://doi.org/10.6345/NTNU202401619
論文種類: 學術論文
相關次數: 點閱:200下載:5
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  • 全向移動平台(Omnidirectional Mobile Platform)是一種具有全方向移動能力的移動平台,比起傳統的四輪平台更加靈活且複雜。本文自行設計此移動平台並結合機械手臂與影像辨識系統,並整合軟、硬體功能,最後使其能夠模擬一些簡單的人體動作。
    在機械手臂方面,描述了手臂的運動模型,取得末端的位置座標;在影像辨識上,利用雙目測距取得球體的世界座標;再將機械手臂與雙目估計的座標整合,最後透過拋物線運動方程式以及類神經網路預測其落點。
    最後通過實驗結果證明所提出的方法可以整合不同的座標系,且可以追蹤球體的座標,及時回傳並移動到預測落點的位置,再控制機械手臂到實際球體落下位置完成接球動作。

    The omnidirectional mobile platform is a type of mobile platform with omnidirectional movement capability, making it more agile and complex compared to traditional four-wheeled platforms. This thesis, designed this mobile platform and integrated it with a robotic arm and an image recognition system to simulate some simple human movements.
    In the field of robotic arms, the motion model of the arm is described to obtain the coordinates of the end effector. In image recognition, binocular distance measurement is used to obtain the world coordinates of the ball. The coordinates estimated by the binocular vision system are then integrated with the robotic arm's coordinates. Finally, through the projectile motion equation and neural network, the landing point of the ball is predicted.
    The experimental results prove that the proposed method can integrate different coordinate systems, and track the coordinates of the sphere, return it in time and move it to the predicted landing point, and then control the robotic arm to the landing position of the sphere to complete the catching action.

    誌 謝 i 摘 要 ii ABSTRACT iii 目 錄 iv 圖 目 錄 vii 表 目 錄 ix 第一章  緒論 1 1.1 研究動機與背景 1 1.2 研究目的 2 1.3 研究方法 3 1.4 論文架構 3 第二章  文獻探討 4 2.1 機械手臂之演進 4 2.2 機械手臂之運動模型 5 2.3 動態物件追蹤 7 第三章  軟、硬體系統架構與設計 10 3.1 系統架構 11 3.2 硬體設備 11 3.3 軟體架構 13 3.3.1 動態物件追蹤系統 14 3.3.2 全向移動平台機械手臂控制系統 15 第四章  動態物件追蹤方法與座標整合 16 4.1 深度估計方法 16 4.1.1 雙目測距 16 4.1.2 相機內部參數 17 4.1.3 相機外部參數 19 4.1.4 立體視覺校正 20 4.1.5 特徵提取 20 4.1.6 視差計算 21 4.1.7 三角測量 22 4.2 水平與垂直位置估計方法 24 4.2.1 多項式擬合 24 4.3 落點位置分析 29 4.3.1 拋物線運動 29 4.3.2 類神經網路 33 4.4 座標整合 36 4.4.1 機械手臂座標轉換 36 4.4.1.1 順向運動學 37 4.4.1.2 逆向運動學 39 4.4.2 雙目辨識座標轉換 41 4.4.3 移動平台座標轉換 41 4.4.4 座標整合 44 第五章  實驗與分析 45 5.1 落點預測實驗 45 5.2 移動平台特性實驗 51 5.3 移動平台結合動態物件追蹤接球實驗 53 5.4 機械手臂接球實驗 59 5.5 移動平台結合機械手臂接球實驗(九個區塊) 62 5.6 移動平台結合機械手臂接球實驗(三個區塊) 68 第六章  結論與展望 71 6.1 結論 71 6.2 未來展望 71 參 考 文 獻 72

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