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中文論文名稱 結合粒子群最佳化法之雙層粒子濾波器於移動機器人的定位與地圖建置
英文論文名稱 Two-Layer Particle Filters Incorporating Particle Swarm Optimization for Mobile Robot Localization and Mapping
校院名稱 淡江大學
系所名稱(中) 電機工程學系博士班
系所名稱(英) Department of Electrical Engineering
學年度 99
學期 1
出版年 100
研究生中文姓名 鄧宏志
研究生英文姓名 Hung-Chih Teng
學號 895440104
學位類別 博士
語文別 中文
口試日期 2011-01-13
論文頁數 128頁
口試委員 指導教授-翁慶昌
共同指導教授-許陳鑑
委員-龔宗鈞
委員-李世安
委員-許陳鑑
委員-王銀添
委員-王偉彥
委員-黃志良
中文關鍵字 定位  地圖建置  粒子濾波器  粒子群最佳化法  同時定位與地圖建置 
英文關鍵字 Localization  Mapping  Particle filter  Particle Swarm Optimization (PSO)  Simultaneous Localization And Mapping (SLAM) 
學科別分類
中文摘要 本論文提出一個結合粒子群最佳化法(PSO)之雙層粒子濾波器架構,並將其應用於移動機器人的定位與地圖建置。在機器人定位的部分,本論文提出一個結合粒子群最佳化法之粒子濾波器架構,透過粒子群演算機制所具備的快速收斂與強大的最佳解搜尋能力等優勢,可增進移動機器人定位的性能。相較於其他的定位方法,本論文所提出的架構能夠更精確的估測得到移動機器人在環境中的位置座標。在地圖建置的部分,本論文以粒子濾波器對環境中的特徵物體進行位置估測,並將粒子濾波器內部的預測機制予以改良,解決特徵物體在估測時無預測資訊輸入的問題,並使粒子具有小幅度的擾動,此改良型的粒子濾波器可提升粒子濾波器在進行地圖建置時估測的正確性。最後將兩者予以整合,建立結合粒子群最佳化法之雙層粒子濾波器於移動機器人定位與地圖建置的系統架構,藉由演化機制的運作可以改善移動機器人定位的性能並提升地圖建置的正確性,實現移動機器人在探索與認知未知環境的能力。模擬與實驗的結果證實,本論文所提出結合粒子群最佳化法之雙層粒子濾波器具有不錯的性能,能夠滿足移動機器人探索未知環境的應用需求。
英文摘要 In this dissertation, architecture of two-layer particle filters incorporating particle swarm optimization (PSO) is proposed and applied on the localization and mapping of mobile robot. For the robot localization, the particle filter is modified by integrating a particle swarm optimization algorithm, where the excellent performance in global optimization of the PSO is used to improve the localization performance. In comparison with conventional particle filters, the proposed particle filter can better determine the robot’s position. For the map building, the particle filter is applied to estimate position of landmarks in the environment, in which the prediction step in the filter is modified by adding small random perturbations into the particles. As a result, the proposed method can better determine position of landmarks. By combining these two functionalities, the architecture of two-layer particle filters is proposed to investigate the localization and mapping of the mobile robot simultaneously. Due to the incorporation of the PSO, the proposed architecture is capable of reducing the localization error of the robot while improving the mapping accuracy of the landmarks. As a result, the robot can better explore an unknown environment with the proposed architecture. Simulation and experimental results show that the proposed approach has a better performance for the localization and mapping of the mobile robot.
論文目次 目錄
目錄 I
圖目錄 IV
表目錄 VII
第一章 序論 1
1.1 研究背景 1
1.2 研究目的 7
1.3 論文架構 11
第二章 感知模型建立與地圖建置 13
2.1 機率學與理論基礎 13
2.2 感測器模型建立 21
2.3 里程計模型建立 28
2.4 資訊融合與地圖建置 30
第三章 結合最佳化法之粒子濾波器 33
3.1 應用粒子濾波器於機器人定位 34
3.2 粒子群最佳化法 43
3.3 結合粒子群最佳化法之粒子濾波器 50
3.4 Nelder-Mead單體搜尋法 56
3.5 結合Nelder-Mead單體搜尋法之粒子濾波器 61
3.6 實驗分析 64
第四章 機器人定位與地圖建置 71
4.1 結合粒子群最佳化法之粒子濾波器於機器人定位 72
4.2 應用粒子濾波器於機器人地圖建置 77
4.3 結合粒子群最佳化法之雙層粒子濾波器於機器人定位與地圖建置 85
第五章 模擬與實驗分析 92
5.1 模擬分析 92
5.2 實驗分析 97
第六章 結論與未來展望 106
6.1 結論 106
6.2 未來展望 107
附錄 109
附錄A Box-Muller轉換(Box-Muller Transform) 109
參考文獻 115
研究著作 123
期刊論文 123
會議論文 123
學位論文 125
研究報告 125
獲獎經歷 127


  
圖目錄
圖1.1、機器人導航三大重點研究方向領域之示意圖 2
圖1.2、機器人定位與地圖建置功能之運作概念示意圖 8
圖1.3、機器人定位與地圖建置之運作流程圖 10
圖2.1、遞迴式貝氏濾波器於機器人定位之運作示意圖 20
圖2.2、非理想感測器現象之機率密度分布圖 23
圖2.3、非預期障礙物現象之機率密度分布圖 24
圖2.4、感測器隨機雜訊現象之機率密度分布圖 26
圖2.5、感測器失效現象之機率密度分布圖 27
圖2.6、典型距離感測器偵測環境資訊之機率密度分布圖 28
圖2.7、典型機器人的位移與里程計模型之示意圖 29
圖2.8、考慮環境 的遞迴式貝氏濾波器於機器人定位之運作示意圖 32
圖3.1、機率密度函數分布與粒子分佈密度之關聯示意圖 35
圖3.2、貝氏濾波器與粒子濾波器樣本狀態運作之關聯示意圖。 以機率密度函數呈現(a)感測器模型、與(b)里程計模型之機率分布, 以粒子權重呈現(c)感測器模型、(d)里程計模型、(e)混合之機率分布 36
圖3.3、傳統粒子濾波器重新取樣步驟之演算程式碼 38
圖3.4、傳統粒子濾波器應用於機器人定位之運作流程圖 39
圖3.5、粒子濾波器應用於機器人定位運作步驟之分解示意圖。 (a)權重正規化與重新取樣,(b)狀態預測, (c)權重更新,(d)權重正規化與重新取樣 41
圖3.6、粒子濾波器應用於機器人定位之疊代過程示意圖 42
圖3.7、粒子群最佳化法之運作流程圖 45
圖3.8、機器人定位問題與函數最佳解問題之關聯示意圖。 (a)機器人運用環境感測器獲取感測資訊, (b)以灰階深淺分布表示感測資訊與該位置環境的相似程度 52
圖3.9、結合粒子群最佳化法之粒子濾波器於機器人定位之運作流程圖 54
圖3.10、NM單體搜尋法的反射步驟之運作示意圖 58
圖3.11、NM單體搜尋法的擴張步驟之運作示意圖 59
圖3.12、NM單體搜尋法的收縮步驟之運作示意圖。 (a)當反射點 的權重值優於最差點 時, (b)當最差點 的權重值優於反射點 時 60
圖3.13、NM單體搜尋法之運作流程圖 61
圖3.14、結合NM單體搜尋法之粒子濾波器於機器人定位之運作流程圖 62
圖3.15、自行研製的全自主足球機器人之外觀實體圖 64
圖3.16、傳統粒子濾波器實現足球機器人定位之移動軌跡圖 67
圖3.17、結合NM單體搜尋法之粒子濾波器實現 足球機器人定位之移動軌跡圖 67
圖3.18、結合粒子群最佳化法之粒子濾波器實現 足球機器人定位之移動軌跡圖 68
圖4.1、機器人狀態與座標系統之關係示意圖 73
圖4.2、結合粒子群最佳化法之粒子濾波器於機器人定位 之系統架構圖 75
圖4.3、機器人狀態與特徵物體位置之座標系統關係示意圖 79
圖4.4、應用粒子濾波器於機器人地圖建置之系統架構圖 81
圖4.5、應用粒子濾波器於機器人地圖建置之運作流程圖 84
圖4.6、應用雙層粒子濾波器於機器人定位與地圖建置之系統架構圖 87
圖4.7、結合粒子群最佳化法之雙層粒子濾波器於 機器人定位與地圖建置之系統架構圖 88
圖4.8、結合粒子群最佳化法之雙層粒子濾波器於 機器人定位與地圖建置之運作流程圖 91
圖5.1、應用雙層粒子濾波器於機器人定位與地圖建置之模擬結果圖 94
圖5.2、結合粒子群最佳化法之雙層粒子濾波器 於機器人定位與地圖建置之模擬結果圖 95
圖5.3、自主移動機器人之硬體外觀與架構實體圖 98
圖5.4、結合粒子群最佳化法之雙層粒子濾波器於自主移動機器人 定位與地圖建置之實驗過程分解示意圖 101
圖5.5、應用雙層粒子濾波器於自主移動機器人定位與地圖建置 之實驗結果圖 102
圖5.6、結合粒子群最佳化法之雙層粒子濾波器於自主移動機器人 定位與地圖建置之實驗結果圖 103
圖A.1、常態分布曲線 110


  
表目錄
表1.1、圖1.2使用符號之說明表 9
表2.1、第2.1章節使用符號之彙整表 21
表3.1、應用粒子濾波器於機器人定位模擬之參數設定列表 40
表3.2、第3.2章節使用符號之彙整表 50
表3.3、第3.3章節模擬實驗參數之規劃說明表 51
表3.4、自行研製的全自主足球機器人之規格彙整表 65
表3.5、足球機器人定位實驗之參數列表 66
表3.6、改良式粒子濾波器與傳統粒子濾波器 實現足球機器人定位之分析比較表 70
表4.1、第4.1章節使用符號之彙整表 77
表4.2、第4.2章節使用符號之彙整表 85
表5.1、機器人定位與地圖建置模擬之參數列表 93
表5.2、機器人定位模擬結果之數據分析表 96
表5.3、機器人地圖建置模擬結果之數據分析表 97
表5.4、自主移動機器人定位與地圖建置實驗 之七個特徵物體顏色與座標位置參數表 98
表5.5、自主移動機器人定位與地圖建置之實驗參數 100
表5.6、自主移動機器人定位實驗之數據分析表 104
表5.7、自主移動機器人地圖建置實驗之數據分析表 105
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