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系統識別號 U0002-0108201117021300
中文論文名稱 隨機散佈障礙環境下動態路徑規劃–結合GVD、D* Lite、與SVM之研究
英文論文名稱 Dynamic Path Planning under Randomly Distributed Obstacle Configuration – a Study Based on GVD, D* Lite and SVM
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 99
學期 2
出版年 100
研究生中文姓名 蕭孟華
研究生英文姓名 Meng-Hua Shiao
學號 698371191
學位類別 碩士
語文別 中文
口試日期 2011-07-12
論文頁數 162頁
口試委員 指導教授-楊智旭
共同指導教授-楊棧雲
委員-連豊力
委員-楊智旭
委員-楊棧雲
委員-王銀添
委員-孫崇訓
中文關鍵字 路徑規劃  廣義Voronoi結構劃分  D* Lite演算法  支持向量機  平滑  安全 
英文關鍵字 Path Planning  Generalized Voronoi Tessellation  D* Lite  SVM  Smooth  Safe 
學科別分類 學科別應用科學機械工程
中文摘要 本論文針對隨機散佈障礙環境下,安全平滑之動態路徑更新,結合非格點式之拓樸地圖,建立快速之路徑再規劃方法,以符合動態路徑規劃之需求,基於非格點式拓樸地圖,重新檢驗、修改廣義Voronoi結構劃分(GVT, Generalized Voronoi Tessellation)與D* Lite演算法(D* Lite Algorithm)之理論,確立一個時間精省具有再規劃能力之前處理器,再以支持向量機(SVM, Support Vector Machine)構築一個具最大邊限的安全平滑路徑。
本研究援引之廣義Voronoi結構劃分與D* Lite演算法皆為計算時間考量下一時之選,銜接非格點式之拓樸地圖,環環相扣,奠定一計算精省之再規劃方法,具有實際應用之潛力,再佐以支持向量機之平滑後處理,確可達於隨機散佈障礙環境下安全平滑之動態路徑更新的目的。
英文摘要 In real applications, a path planning under dynamic environment change is a practical and important topic for the grounded mobile robot. The study elementarily employs the methods of generalized Voronoi tessellation (GVT) and D* Lite shortest path algorithm for the topic. With the excellence of less computation, the topology of scatted waypoint configuration for mapping the terrain is first chosen for the real-time replanning request. The elementary methods are hence theoretically modified to fit adequately the waypoint topology. The modification seeks a swift solution to respond a replanning request corresponding to a waypoint topology change. The sub-sequential processes: waypoint topology change, GVT, and D* Lite form a pre-processing planner. It can be applied standalone to provide an optimized piecewise linear path for robot reaction. With an additional support vector machine (SVM) post-processing smoother, it can also provide a wide-margin safe and smooth path. The smooth path is optimized, too. The solid development based on the waypoint topology, instead of the quantized grid-map of previous researchers, is the most important contribution of the study. With small scaled experiments, evidential results show consistently the model is promising for a great improvement in applications and achieve the original goal of the study.
論文目次 目錄
誌謝I
中文摘要II
英文摘要III
目錄V
圖目錄VIII
表目錄XII
第一章 緒論1
1-1、 前言1
1-2、 研究動機與目的3
1-3、 相關文獻3
1-4、 本研究特色5
第二章 基礎理論7
2-1、 地圖格式表示7
2-1-1、 特徵地圖7
2-1-2、 拓樸地圖13
2-2、 廣義Voronoi結構劃分(GVT)16
2-3、 內點判別演算法20
2-4、 路徑搜尋演算法24
2-4-1、 Dijkstra演算法25
2-4-2、 A*演算法25
2-4-3、 D*演算法27
2-4-4、 LPA*演算法27
2-4-5、 D* Lite演算法28
2-5、 地圖變動偵測29
2-6、 支持向量機(SVM)30
第三章 研究設備與方法35
3-1、 研究設備35
3-2、 研究方法38
3-2-1、 機器人導航與可移動障礙物40
3-2-2、 路徑初規劃41
3-2-2-1、 廣義Voronoi結構劃分41
3-2-2-2、 D* Lite產生最短路徑PVDL 44
3-2-2-3、 拓樸單點指定標籤51
3-2-2-4、 PSVM平滑路徑52
3-2-3、 路徑再規劃53
3-2-3-1、 拓樸更新與地圖資訊交接53
3-2-3-2、 D* Lite演算法重新找尋路徑55
3-2-3-3、 指定類別標籤與產生新的平滑路徑PSVM56
第四章 實驗與討論58
4-1、 非格點地圖路徑再規劃實例58
4-2、 格點與非格點地圖路徑再規劃比較77
4-3、 非格點地圖之D* Lite與A*路徑再規劃之比較101
4-4、 障礙物複雜度於非格點地圖路徑規劃之比較112
第五章 結論與討論115
參考文獻116
附錄121

圖目錄
圖1-1 安全平滑路徑規劃之需求實例2
圖1-2研究步驟流程圖6
圖2-1 地圖格式化與特徵地圖之範例9
圖2-2 特徵地圖配置實例11
圖2-3 網路連通拓樸地圖示意圖14
圖2-4 路徑點連結之拓樸地圖示意圖15
圖2-5 Voronoi結構劃分17
圖2-6 一般與廣義之Voronoi 結構劃分18
圖2-7 區塊障障物內Voronoi頂點與邊之剪除19
圖2-8 射線內點判別法21
圖2-9 射線內點判別演算法交點判斷法則23
圖2-10 支持向量機邊限示意圖34
圖3-1系統場地模擬圖36
圖3-2 場地實際場景圖36
圖3-3 組裝完成之NXT機器人38
圖3-4 系統流程圖39
圖3-5 廣義Voronoi結構劃分之拓樸地圖44
圖3-6 粗規劃之最短路徑PVDL50
圖3-7 障礙物貼上標籤的原則程序51
圖3-8 PVDL與障礙點指定標籤52
圖3-9 支向機之平滑曲線圖53
圖3-10 環境變動後產生之新拓樸地圖54
圖3-11 環境變動後產生新的粗路徑56
圖3-12 環境變動後重新指定類別標籤57
圖3-13 環境變動後更新之SVM平滑路徑57
圖4-1 圖書館樓層示意圖58
圖4-2 Voronoi結構劃分59
圖4-3 廣義Voronoi結構劃分60
圖4-4 D* Lite由起點開始拓展,並將鄰近節點加入待拓展集合U中61
圖4-5 由集合U中找出最小成本節點並以此拓展地圖資訊62
圖4-6 拓展完之節點達到區域一致性(灰色節點)63
圖4-7 找出集合U最小成本之節點64
圖4-8 持續拓展,以達到區域一致性為目標64
圖4-9 D* Lite演算法示意65
圖4-10 D* Lite演算法示意圖65
圖4-11 地圖資訊拓展完成66
圖4-12 由起點追溯最短路徑直到抵達終點67
圖4-13 SVM平滑路徑68
圖4-14 圖書館樓層環境變化示意圖69
圖4-15 新產生之廣義Voronoi圖形70
圖4-16 新舊地圖交接示意圖71
圖4-17 舊地圖資訊成功交接至新地圖72
圖4-18 由集合U找出更新後具最小成本之節點73
圖4-19 D* Lite演算法示意圖73
圖4-20 D* Lite演算法示意圖74
圖4-21 更新後地圖資訊拓展完成74
圖4-22 由起點追溯最短路徑直到抵達終點75
圖4-23 路徑再規劃之SVM76
圖4-24 案例一之格點地圖實際場景與路徑規劃結果80
圖4-25 案例一之非格點地圖實際場景與路徑規劃結果81
圖4-26 案例二之格點地圖實際場景與路徑規劃結果83
圖4-27 案例二之非格點地圖實際場景與路徑規劃結果84
圖4-28 案例三之格點地圖實際場景與路徑規劃結果86
圖4-29 案例三之非格點地圖實際場景與路徑規劃結果87
圖4-30 案例四之格點地圖實際場景與路徑規劃結果89
圖4-31 案例四之非格點地圖實際場景與路徑規劃結果90
圖4-32 案例五之格點地圖實際場景與路徑規劃結果93
圖4-33 案例五之非格點地圖實際場景與路徑規劃結果94
圖4-34 案例六之格點地圖實際場景與路徑規劃結果96
圖4-35 案例六之非格點地圖實際場景與路徑規劃結果97
圖4-36 案例七之格點地圖實際場景與路徑規劃結果99
圖4-37 案例七之非格點地圖實際場景與路徑規劃結果100
圖4-38 案例一之D* Lite路徑規劃實際場景與路徑規劃結果104
圖4-39 案例一之A*路徑規劃實際場景與路徑規劃結果105
圖4-40 案例二之D* Lite路徑規劃實際場景與路徑規劃結果107
圖4-41 案例二之A*路徑規劃實際場景與路徑規劃結果108
圖4-42 案例三之D* Lite路徑規劃實際場景與路徑規劃結果110
圖4-43 案例三之A*路徑規劃實際場景與路徑規劃結果111
圖4-44 非格點地圖於不同數量障礙物之路徑搜尋時間折線圖114

表目錄
表2-1 不同障礙物與虛擬點之定義10
表2-2 對應圖2-2之特徵地圖座標與單點性質辨識碼12
表3-1 Voronoi節點資料型態43
表4-1案例一之格點與非格點地圖路徑再規劃比較79
表4-2案例二之格點與非格點地圖路徑再規劃比較82
表4-3案例三之格點與非格點地圖路徑再規劃比較85
表4-4案例四之格點與非格點地圖路徑再規劃比較88
表4-5案例五之格點與非格點地圖路徑再規劃比較92
表4-6案例六之格點與非格點地圖路徑再規劃比較95
表4-7案例七之格點與非格點地圖路徑再規劃比較98
表4-8 案例一之D* Lite與A*演算法路徑規劃實驗記錄103
表4-9 案例二之D* Lite與A*演算法路徑規劃實驗記錄106
表4-10 案例三之D* Lite與A*演算法路徑規劃實驗記錄109
表4-11 非格點地圖於不同障礙物混雜度之拓樸地圖轉換時間113
表4-12 非格點地圖於不同障礙物混雜度D* Lite路徑搜尋時間113
表4-13 非格點地圖於不同障礙物數量之路徑搜尋時間比較114
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