系統識別號 | U0002-0108201117021300 |
---|---|
DOI | 10.6846/TKU.2011.00023 |
論文名稱(中文) | 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 |
論文名稱(英文) | Dynamic Path Planning under Randomly Distributed Obstacle Configuration – a Study Based on GVD, D* Lite and SVM |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 機械與機電工程學系碩士班 |
系所名稱(英文) | Department of Mechanical and Electro-Mechanical Engineering |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 99 |
學期 | 2 |
出版年 | 100 |
研究生(中文) | 蕭孟華 |
研究生(英文) | Meng-Hua Shiao |
學號 | 698371191 |
學位類別 | 碩士 |
語言別 | 繁體中文 |
第二語言別 | |
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 |
關鍵字(中) |
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 |
關鍵字(英) |
Path Planning Generalized Voronoi Tessellation D* Lite SVM Smooth Safe |
第三語言關鍵字 | |
學科別分類 | |
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. |
第三語言摘要 | |
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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