博碩士論文 975202015 詳細資訊




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姓名 徐子勝(Tzu-sheng Hsu)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 以立體視覺為基礎之機械手臂應用系統
(A Stereo Vision-Based Robot Arm system and its applications)
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摘要(中) 本論文提出了一套機械手臂之應用系統,本系統設計一具有5個自由度(degree of freedom)之機械手臂,機械手臂前端有夾具可以抓取空間中的目標物,並以雙攝影機實現目標物追蹤。為了由影像追蹤目標物,作者提出一個以立體視覺為基礎之機械手臂定位方法,首先透過攝影機校正方法(camera calibration)求出雙攝影機之內外部參數予以校正,並結合雙眼立體視覺(binocular stereo vision)求出物體在世界座標系統中實際重心位置。雖然物體的重心位置經過校正,但在估算與攝影機中心的距離時仍有些微誤差,因此本系統利用多層感知機(Multilayer perceptrons)修正此誤差。接著計算出順向運動學(Forward kinematics)所得之端點座標與目標物實際位置之誤差值,並以其誤差值當作基因演算法(Genetic Algorithms)的適應函數 (fitness function)推算出機械手臂中各馬達的角度,達到機械手臂定位之目的。結果驗證部分,我們將目標物放在隨機放置在機械手臂的工作範圍內,並透過上述演算法將目標物夾取到目的地。
摘要(英) This thesis presents a vision-based robot arm with 5 degrees of freedom and an end-effector attached to the end of the robot arm for grasping an object. In order to locate a target from the image, a stereo vision-based robot arm system is implemented. A stereo calibration algorithm is adopted for estimating the two cameras’ intrinsic and extrinsic parameters. With the estimated parameters, the stereo-vision system can estimate the 3D position of the object in the world coordinate system. A trained multi-layer perception is then used for compensating the location estimation errors incurred by the inaccurate parameters estimated from the calibration procedure. In the following, the Genetic Algorithms (GA) is adopted to solve the forward kinematics problem in order to compute the angles of each motor. The performance of the robot arm was tested by several real-life experiments of griping a target at different positions.
關鍵字(中) ★ 多層感知機
★ 雙眼立體視覺
★ 基因演算法
★ 攝影機校正
關鍵字(英) ★ Genetic Algorithm
★ binocular stereo vision
★ camera calibration
★ MLP
論文目次 第一章緒論.......................................1
1-1 研究動機.....................................1
1-2 研究目的.....................................2
1-3 論文架構.....................................2
第二章相關研究...................................3
2-1機器視覺應用機械手臂相關......................3
第三章硬體介紹...................................8
3-1 機械手臂馬達及機構...........................9
3-1-1 馬達介紹...................................10
3-1-2機械手臂結構................................12
3-2 SSC-32馬達控制器.............................16
第四章系統架構...................................17
4-1立體視覺測距..................................18
4-1-1 亮度矯正...................................18
4-1-2 K-means群聚演算法..........................20
4-1-3 立體比對(Stereo match).....................25
4-1-4 距離估測...................................28
4-1-5相機參數取得................................31
4-2 以MLP解誤差..................................33
4-3機械手臂取物..................................38
4-3-1 機械手臂之正向運動學.......................40
4-3-2反向運動學..................................42
4-3-2-1 一般反向運動學...........................42
4-3-2-2 基因演算法用於反向運動學.................45
4-4語音辨識模組..................................49
第五章實驗.......................................51
5-1 立體視覺測距.................................51
5-2 機械手臂定位.................................54
5-3 以立體視覺控制機械手臂取物...................55
5-4 語音控制機械手臂取物.........................58
第六章結論與未來展望.............................61
6-1 結論.........................................61
6-2 未來展望.....................................62
參考文獻.........................................63
附錄一 立體視覺測距實驗表........................66
附錄二 立體視覺測距實驗表........................73
附錄三 機械手臂定位實驗表........................79
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指導教授 蘇木春(Mu-Chun Su) 審核日期 2010-7-23
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