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研究生： 崔嶽
Yue Tusi
論文名稱： 使用人工蜂群演算法和快速搜索隨機樹改進路徑規劃系統之研究
Using ABC and RRT Algorithms to Improve Path Planning
指導教授： 鍾鴻源
Hung-Yuan Chung
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2016
畢業學年度： 104
語文別： 中文
論文頁數： 109
中文關鍵詞： 人工蜂群演算法 、快速搜索隨機樹 、移動機器人 、路徑規劃 、危險度
外文關鍵詞： Artificial bee colony, Rapidly-exploring Random Tree, Path Planning, Mobile Robot, Danger Degree Map
相關次數： 點閱：23  下載：0 
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    • 本研究以人工蜂群演算法(ABC)結合快速搜索隨機樹(RRT)來創新路徑規劃演算法應用在移動式機器人當中，路徑規劃對於移動機器人是非常重要的研究之一，本文目的是如何在有障礙物的環境中，規劃出一條適合行走且無危險和有效率的路徑，讓機器人從起始點移動到目標點是安全且正確的。
    與傳統的演算法有所不同，本文是先用RRT演算法的方式來尋找延伸點，在數個延伸點經過我們比較之後，選擇最佳的延伸點來使蜜蜂移動，因為人工蜂群演算法擁有結構簡單、容易操作且收斂速度較快的性能，他改善了以往用於路徑規劃的演算法在收斂速度慢和容易陷入區域最佳點的問題，雖然RRT在搜索未知區域方面有優良的特性，但它在每次規劃路徑上是不穩定的，所以本文結合人工蜂群的特性加入到RRT的演算法上，並處理障礙物的問題來模擬機器人的路徑。
    總而言之，本文提出改良的演算法較以往單獨的RRT或單獨的人工蜂群演算法更具有效率與穩定且路徑最短。

    In this study, we use the Artificial Bee Colony algorithm incorporating the fast search Random Tree (RRT) for innovation path planning, and is implemented in a mobile robot. Path planning for mobile robot is one of the very important researches. The aim is how to plan a path in the environment which have obstacles, and make the robot walk from start point to target safely and correctly.
    Different from conventional algorithms, we use the RRT algorithm to find several extending points, and after comparison we choose the best extending point to make the extension of bees move. Because Artificial Bee Colony algorithm is simple structure, easy operation and fast convergence, it improved the problem of path planning which in the slow convergence and easy to local optimal solution in previous path planning algorithms. While the RRT has excellent characteristics in seeking unknown area, it is unstable for each planning. We herein combine the characteristics of artificial bee colony with the RRT algorithms to deal with the problem of obstacles and make practical simulation in a mobile robot.
    In summary, it is shown that the data of the new algorithm on path planning are much more effective and stable than those of either single ABC or single RRT, and the path is the shortest.

    目錄 中文摘要 i Abstract ii 誌謝 iii 目錄 iv 圖目錄 vii 表目錄 ix 第一章 緒論 1 1-1 簡介 1 1-2 研究背景 2 1-3 文獻探討 3 1-4成果與主要貢獻 5 1-5論文架構 5 第二章 路徑規劃的問題定義與敘述 6 2-1定義路徑規劃 6 2-2路徑規劃的種類 7 2-3全域路徑規劃方法 7 2-3-1 Dijkstra’s演算法 8 2-3-2 A*演算法 9 2-3-3 拓樸法 10 2-3-4 可視圖法(visibility graph) 11 2-3-5 柵欄法 13 2-4 局部規劃路徑方法 14 2-4-1模擬退火法 14 2-4-2人工勢場法 14 2-4-3模糊控制算法 15 2-4-4類神經網路 16 2-5 本篇論文的環境設定 16 第三章 本篇論文使用的方法與問題分析 22 3-1 快速擴展隨機樹演算法(RRT) 22 3-2 雙向快速搜尋隨機樹 26 3-3 人工蜂群演算法(ABC) 28 3-3-1 人工蜂群演算法背景 28 3-3-2 人工蜂群的行為模式 28 3-3-3 人工蜂群的數學模式 31 3-3-4 人工蜂群演算法的程序 33 3-3-5 人工蜂群演算法的行為和參數設置 36 3-4 障礙物的危機處理 37 第四章 路徑規劃的改進 39 4-1改進方法(I) 40 4-1-1理念架構 40 4-1-2適應函數與懲罰值的問題 44 4-2 改進方法(II) 48 4-2-1演算法如何選擇延伸點 48 4-2-2改進方法(II)的流程 49 4-3改進延伸點的選擇方式 55 4-4改良人工蜂群演算法加上快速搜索擴展隨機樹算法步驟 61 第五章 實驗結果與討論 65 5-1演算法和地圖介紹 65 5-2 ABC混合型演算法細節討論 69 5-2-1 蜜蜂的數目和疊代次數的關係 69 5-2-2延伸點的設定 70 5-3演算法在簡單地圖中的情形 72 簡單地圖 73 5-4 演算法在複雜地圖上的情形 77 複雜地圖1 78 複雜地圖2 83 5-5危險度機制的加入 87 5-6動態環境測試 89 第六章 結論 92 6-1總結 92 6-2未來建議 93 參考文獻 94   圖目錄 圖1-1 Dijkstra演算法和A*演算法比較圖 4 圖2-1 Dijkstra’s演算法擴散圖 8 圖2-2 有向圖 9 圖2-3 A*搜尋演算法示意圖 10 圖2-4 拓樸法建立拓樸網路示意圖 11 圖2-5 visibility graph示意圖 12 圖2-6 Voronoi 圖法示意圖 13 圖2-7 人工位能場所規劃的路徑 15 圖2-8 欄格環境 18 圖2-9 結構圖 19 圖2-10 本文所設定的簡單環境 20 圖3-1 基本RRT生長圖 23 圖3-2 RRT數據結構 25 圖3-3 雙向擴展的RRT示意圖 27 圖3-4 蜜蜂的覓食行為 31 圖3-5 人工蜂群演算法流程圖 35 圖3-6 加入安全領域和之前地圖的比較 37 圖3-7 危險度地圖 38 圖4-1 在不同疊代次數下經過改進方法(I)的示意圖 42 圖1-2 兩點間障礙物的判斷方向 45 圖4-3 改進方法(II)中延伸點的選擇方式示意圖 49 圖4-4 新的延伸點產生範圍示意圖 52 圖4-5 延伸點取樣範圍隨著疊代次數增加而縮小曲線圖 52 圖4-6 改進方法(II)的流程圖 53 圖4-7 延伸點延伸時可能發生的情況 55 圖4-8 加入新方法的延伸點選擇示意圖 58 圖4-9 陷入局部最佳解的示意圖 59 圖4-10 延伸點的可能擴展方向 60 圖4-11 人工蜂群演算法加上RRT流程圖 63 圖5-1 程式介面 65 圖5-2 簡單地圖環境 66 圖5-3 複雜地圖環境1 67 圖5-4 複雜地圖環境2 68 圖5-5 簡單地圖中ABC在不同疊代次數下模擬圖 73 圖5-6 簡單地圖中AFSA在不同疊代次數下模擬圖 74 圖5-7 簡單地圖中HPSO-TVAC在不同疊代次數下模擬圖 75 圖5-8 簡單地圖中PSO在不同疊代次數下模擬圖 76 圖5-9 複雜地圖1中PSO在不同疊代次數下模擬圖 78 圖5-10 複雜地圖1中HPSO-TVAC在不同疊代次數下模擬圖 79 圖5-11 複雜地圖1中AFSA在不同疊代次數下模擬圖 80 圖5-12 複雜地圖1中ABC在不同疊代次數下模擬圖 81 圖5-13 複雜地圖2中PSO在不同疊代次數下模擬圖 83 圖5-14 複雜地圖2中HPSO-TVAC在不同疊代次數下模擬圖 84 圖5-15 複雜地圖2中AFSA在不同疊代次數下模擬圖 85 圖5-16 複雜地圖2中ABC在不同疊代次數下模擬圖 86 圖5-17 在複雜地圖1中加入危險度的ABC演算法比較圖 88 圖5-18 在複雜地圖2中加入危險度的ABC演算法比較圖 89 圖5-19 動態環境地圖 90 圖5-20 不同疊代次數在動態環境下的模擬圖 91 表目錄 表3-1 傳統RRT演算法的pseudo code 26 表4-1 改進疊代次數的執行函式 43 表4-2 懲罰值執行函式 47 表5-1 路經規劃演算法參數 69 表5-2 蜜蜂數目和疊代時間對照表 70 表5-3 有無固定延伸點的對照表 70 表5-4 延伸點的產生與疊代次數關係表 71 表5-5 四種演算法在簡單地圖上的比較 77 表5-6 四種演算法在複雜地圖1上的比較 82 表5-7 四種演算法在複雜地圖2上的比較 87

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