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中文論文名稱 以全方位視覺之特徵掃描比對方法進行機器人自我定位
英文論文名稱 Robot Self-Localization using Feature Scan Matching with an Omni-directional Vision
校院名稱 淡江大學
系所名稱(中) 機械與機電工程學系碩士班
系所名稱(英) Department of Mechanical and Electro-Mechanical Engineering
學年度 95
學期 2
出版年 96
研究生中文姓名 陳敬宏
研究生英文姓名 Jing-Hong Chen
學號 694340455
學位類別 碩士
語文別 中文
口試日期 2007-07-20
論文頁數 60頁
口試委員 指導教授-王銀添
委員-張文中
委員-李祖聖
委員-楊智旭
委員-翁慶昌
委員-劉昭華
中文關鍵字 全方位視覺  蒙地卡羅自我定位方法 
英文關鍵字 omni-directional vision  Monte Carlo localization 
學科別分類 學科別應用科學機械工程
中文摘要 本論文規劃足球機器人的蒙地卡羅自我定位方法,所使用的機器人具備全方位視覺與全方位驅動輪。運動模型方面,以全方位驅動裝置的反向運動矩陣相對時間積分,搭配回授的里程計訊息建立運動模型。感測模型方面,以機器人位置為中心,規劃全方位影像徑向的像素掃描線。將像素掃描線偵測到的特徵,以高斯分佈方式建立感測模型的比對資料庫。機器人行進中執行自我定位時,交互使用運動模型與感測模型修正機器人在環境中的位置信念(position belief)。
英文摘要 In this thesis, we use Monte Carlo localization (MCL) algorithms to solve the self-localization problem for robots and apply to soccer robots which have an omni-directional vision system and an omni-directional-driven mechanism. The differential kinematics equation for the omni-directional drive is derived to construct the motion model of MCL. In the motion model, an optical encoder is utilized as the odometer sensor. For the sensor model of MCL, an omni-directional vision system is mounted on the center of the robot to detect color features of the environment. The database of the color features is built for the feature scan matching by adopting the method of mixture probability distribution. After the MCL algorithms are developed, the robot system can locate its position in the environment by updating its position belief recursively, according to the motion model and the sensor model of MCL algorithms.
論文目次 目錄

中文摘要 I
英文摘要 II
目錄 III
圖目錄 V
表目錄 VIII
第一章 序論 1
1.1研究動機 1
1.2相關文獻探討 2
1.2.1全方位驅動機器人相關文獻 2
1.2.2 自我定位相關論文 3
1.3研究範圍 3
1.4論文架構 4

第二章 蒙地卡羅自我定位方法 5

第三章 機器人運動模型與視覺模型 9
3.1機器人運動方程式 9
3.2里程計(Odometry)之運動模型建立 11
3.3建立感測模型之各種感測機制 17
3.4機器視覺之感測模型建立 19
3.4.1 機率網格建立 20
3.4.2 以全方位攝影機仿效雷射測距儀 21
3.4.3 混合機率分佈 22
3.4.4 感測模型定位結果 25

第四章 移動機器人系統架構 29
4.1移動平台驅動系統 30
4.2 PIC馬達控制器 32
4.3分散式馬達控制系統 33
4.4傳輸命令格式設計 35
4.5轉速控制 38
4.6運算系統 39
4.7視覺感測系統 39

第五章 實測分析 41
5.1 機器人動態定位 41
5.2 強健性測試 44
5.3自我定位之路徑規劃 47

第六章 結果與討論 49
6.1 研究成果 49
6.2 未來研究方向 49

參考文獻 52
相關網頁 56
附錄A 蒙地卡羅法簡介 57



圖目錄

圖1.1 RHINO [Thrun et al.1998] 3
圖1.2 Minerva [Roy et al. 1999] 3
圖2.1重新取樣流程 8
圖3.1全方位移動基本架構座標圖 9
圖3.2 1號車輪狀態 10
圖3.3 The University of Michigan 12
圖3.4本實驗驅動平台 12
圖3.5運動控制器 13
圖3.6範例一實測結果 16
圖3.7(a) 圖3.7(a) 範例二實測結果 17
圖3.7(b) 圖3.7(b) 範例二實測結果 17
圖3.8(a) 測距儀相關產品 18
圖3.8(b) 測距儀效果示意圖[Thrun et al. 2000] 18
圖3.9(a) 角度感測範圍 18
圖3.9(b) 感測器陣列式排列 18
圖3.10 CS Freiburg Team [Weigel et al.2002] 19
圖3.11(a) Philip RoboCup Team 19
圖3.11(b) WinKIT fourth generation robots 19
圖3.12 機率網格示意圖 20
圖3.13 左圖為白色邊線的場地特徵,右圖為藍、黃球門的場地特徵 21
圖3.14 掃描線誤判 22
圖3.15 全方位視覺偵測場地邊線 22
圖3.16(a) 無特徵點情況 22
圖3.16(b) 無窮遠掃描情況 22
圖3.17(a) 期望建模情形 23
圖3.17(b) 實際建模情形 24
圖3.18上圖的像素資料統計圖與機率分佈圖 25
圖3.19(a) 第12條掃描線 26
圖3.19(b) 第16條掃描線 26
圖3.19(c) 第20條掃描線 26
圖3.19(d) 第24條掃描線 26
圖3.19(e) 第60條掃描線 27
圖3.20估測姿態對稱 27
圖3.21(a) 第12條掃描線 28
圖3.21(b) 第16條掃描線 28
圖3.21(c) 第20條掃描線 28
圖3.21(d) 第24條掃描線 28
圖3.21(e) 第60條掃描線 28
圖4.1機器人整體系統架構圖 29
圖4.2機器人機構設計圖與實體照 30
圖4.3(a) 驅動輪模組 31
圖4.3(b) 模組爆炸圖 31
圖4.4全方位移動車台機構圖 31
圖4.5(a) 視覺模組 31
圖4.5(b) 視覺模組爆炸圖 31
圖4.6(a) 集中式馬達控制系統示意圖 34
圖4.6(b) 分散式馬達控制系統示意圖 34
圖4.7分散式馬達控制系統架構 34
圖4.8主控端PC( Master )傳輸軟體流程 36
圖4.9從屬端PIC( Slaver )軟體流程 36
圖4.10(a) 傳輸介面 37
圖4.10(b) 電氣特性 37
圖4.11(a)分散式馬達控制電路 37
圖4.11(b) MAX232電路 37
圖4.12速度前饋+PI控制器方塊圖 38
圖4.13無負載轉速控制軌跡圖 38
圖4.14負載轉速控制軌跡圖 38
圖4.15全方位攝影機 39
圖4.16由攝影機上取得的實際影像 39
圖4.17雙曲面鏡 40
圖4.18 QuickCam Pro 4000 40
圖5.1機器人行走路徑 42
圖5.2(a) 第2次遞迴 43
圖5.2(b) 第3次遞迴 43
圖5.2(c) 第4次遞迴 43
圖5.2(d) 第10次遞迴 43
圖5.2(e) 第60次遞迴 43
圖5.3各種粒子數量之定位比較 44
圖5.4 (a) 視覺感測1/8遮蔽 45
圖5.4 (b)視覺感測1/4遮蔽 45
圖5.4 (c)視覺感測1/2遮蔽 45
圖5.5 1/8遮蔽範圍之定位結果 46
圖5.6 1/4遮蔽範圍之定位結果 46
圖5.7 1/2遮蔽範圍之定位結果 47
圖5.8機器人行走路徑 48
圖5.9運動控制實測圖 48
圖5.10路徑ㄧ之實測軌跡圖 49
圖5.11路徑二之實測軌跡圖 49
圖5.12 路徑三之實測軌跡圖 49
圖5.13(a) 第2次遞迴 50
圖5.13(b) 第3次遞迴 50
圖5.13(c) 第5次遞迴 50
圖5.13(d) 第10次遞迴 50
圖5.13(e) 終點位置 50


表目錄

表3.1 三次多項式之三维係數 15
表4.1全方位車輪規格 30
表4.2 直流馬達尺寸 32
表4.3 命令格式 35
表5.1無遮蔽之方均根誤差 44
表5.2 1/8遮蔽之方均根誤差 46
表5.3 1/4遮蔽之方均根誤差 46
表5.4誤差數據 48
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