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研究生: 孫仕勳
Shih-Hsun Sun
論文名稱: 基於能量螞蟻演算法之路徑規劃與其在雲端平台運算的實現
Cloud Computing Realization of an Energy-Based Ant Colony Optimization Algorithm for Path Planning
指導教授: 呂藝光
Leu, Yih-Guang
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2015
畢業學年度: 103
語文別: 中文
論文頁數: 79
中文關鍵詞: 螞蟻演算法路徑規劃雲端
英文關鍵詞: Ant Colony Optimization, Path-Planning, Cloud Computing
DOI URL: https://doi.org/10.6345/NTNU202205514
論文種類: 學術論文
相關次數: 點閱:260下載:0
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  • 本研究以能量觀點並透過針對能量修正的螞蟻演算法來進行最佳路徑路徑規劃。因應電動車在道路上行駛可能面臨爬坡或是各種材質不同的路面,最短路徑並不一定等於最節省能量的路徑,故定義出能量消耗的公式,結合網頁伺服器的運算,如此就讓使用者在行駛中透過可攜式行動裝置得知目前考量最佳能量路徑之下的結果。演算法以Javascript 實作,結合Google Map的地圖資訊,使其能夠應用在網頁顯示並做出路徑規劃。並且為降低瀏覽器的運算壓力,將巨量的運算交給雲端伺服器處理。最後透過實際載具的道路行駛數據,驗證其準確性。

    This thesis introduces an energy-based algorithm of Ant Colony Optimization. The algorithm was implemented in javascript. Combining with Google Map informations, it can be used for real-road path-planning through a web page. The algorithm computation was moved to cloud-computing by using Node.js server, which is able to run javascript algorithm in server-side, to decrease the pressure of browser. Moreover, we examine its accuracy by actual on-road driving experiments.

    摘 要 i ABSTRACT ii 誌 謝 iii 目 錄 iv 圖目錄 vii 表目錄 x 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究方法 3 1.3 研究目的 4 1.4 章節簡述 5 第二章 文獻探討與回顧 6 2.1 路徑規劃 6 2.1.1 路徑問題 6 2.1.2 Dijkstra 演算法 7 2.1.3 A*演算法 7 2.1.4 基因演算法 9 2.2 螞蟻演算法 10 2.2.1 螞蟻演算法起源 10 2.2.2 初始化路徑問題 11 2.2.3 路徑的選擇 11 2.2.4 費洛蒙更新 13 2.2.5 螞蟻演算法流程圖 14 2.2.6 螞蟻演算法改良版 15 第三章 基於能量螞蟻演算法 16 3.1 傳統路徑演算法缺點 16 3.2 基於能量的路徑選擇公式 17 3.3 能量公式 18 3.4 能量費洛蒙更新 19 3.5 演算法虛擬碼 20 3.6 雲端模擬環境建置 21 3.6.1 Google Map與程式語言選用 21 3.6.2 網頁環境 21 3.6.3 地圖節點的建立 22 3.7 能量螞蟻演算法實作架構與API設計 23 3.8 能量螞蟻演算法實作驗證與參數校調 25 3.8.1 地圖節點選定 25 3.8.2 對照組:統一高度路徑分析 26 3.8.3 實驗組:含高度變化路徑分析 27 3.8.4 結果比較與說明 28 3.8.5 本節小結 29 3.9 能量螞蟻演算法道路分析模擬 30 3.9.1 地圖點選擇 30 3.9.2 模擬參數說明與比較方法 30 3.9.3 模擬A 34 3.9.4 模擬B 36 3.9.5 模擬C 38 3.9.6 模擬D 40 第四章 實驗與結果 42 4.1 實驗平台介紹 42 4.2 數據量測方式 43 4.3 Android應用程式撰寫 44 4.4 實驗步驟與載具詳細參數 45 4.5 實驗甲─轉彎能耗實驗 46 4.5.1 實驗地圖選擇 46 4.5.2 路徑說明 46 4.5.3 路徑模擬結果 47 4.5.4 結果與說明 48 4.6 實驗乙─爬坡能耗實驗 51 4.6.1 實驗地圖選擇 51 4.6.2 路徑說明 51 4.6.3 路徑模擬結果 52 4.6.4 結果與說明 53 第五章 結論與未來展望 56 5.1 結論 56 5.2 未來展望 56 參考文獻 57 附錄 61 自傳 67

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