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姓名 姜俊甫(Jyun-Fu Jiang)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 在室內環境中使用ALC-PSO演算法與危險度指標改良RRT路徑規劃
(Using ALC-PSO Algorithm to Improve RRT Path Planning in Indoor Environments with Danger Degree)
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摘要(中) 在移動式機器人中,要如何在有障礙物的環境中,規劃出一條合適的無障礙物的路徑,讓移動機器人能從起始點移動到達目標點且確保路徑是最短的,是一個很重要議題。
本文提出一種最佳路徑規劃方法於移動機器人,利用改良式粒子群演算法也就是老齡化領導者與挑戰者粒子群演算法(ALC-PSO)來模擬快速搜尋隨機樹(RRT),與過去傳統粒子群演算法用於路徑規劃方法有所不同,在本文是藉由樹枝生長的方式來增加延伸點,在我們比較之後,選擇最好的延伸點加到粒子之中,而這是基於使用ALC-PSO演算法的基礎下產生的創新路徑規劃演算法。
這個方法克服了粒子群演算法容易陷入區域最佳點應用於機器人路徑規劃方面上的缺點,而且因為基本的RRT演算法在每次規劃路徑上是不穩定的,所以本文利用模擬RRT演算法的方式來改良ALC-PSO演算法應用在路徑規劃上,且加入了危險度地圖的概念來避開障礙物,經過模擬結果,我們可以證明這個改良創新演算法可以使結果穩定在室內環境中而且比RRT演算法更好,同時也確保規劃路徑會是最短的。
摘要(英) Path planning is an important issue in mobile robotics. In an environment with obstacles, path planning is to find a suitable collision-free path for a mobile robot to move from a start location to a target location along the shortest path.
This paper proposes an optimal path planning algorithm for mobile robots based on Particle Swam Optimization with an Aging Leader and Challengers (ALC-PSO) to imitate Rapidly-exploring Random Tree (RRT), traditional Particle Swam Optimization (PSO) for path planning is different, In this paper, we propose a branches-grow method based on the ALC-PSO algorithm, and add extend point to particles after we compare.
This method overcomes the drawback for particle swam optimization is easy to fall into local optimization in robotic path planning. Because the basic Rapidly-exploring Random Tree (RRT) path planning is unstable for every time, so this paper improved algorithm of ALC-PSO to imitate RRT in path planning, and add Danger Degree Map to avoid obstacles. From the results of simulations, we show that this algorithm can improve the stability of RRT path planning in static environment, and ensures that the path is almost optimal.
關鍵字(中) ★ 老齡化領導者與挑戰者粒子群演算法
★ 危險度指標
★ 快速搜尋隨機樹
★ 路徑規劃
★ 移動機器人
關鍵字(英) ★ ALC-PSO
★ Danger Degree
★ Rapidly-exploring Random Tree
★ Path Planning
★ Mobile Robots
論文目次 中文摘要 i
英文摘要 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 1
1-2 研究目的與動機 2
1-3 文獻探討 4
1-3-1 Dijkstra 演算法與A*演算法及RRT演算法之比較 4
1-3-2 粒子群演算法的研究進展 5
1-4 成果與主要貢獻 6
1-5 論文架構 7
第二章  研究問題定義與描述 8
2-1 路徑規劃的定義和作用 8
2-2 路徑規劃的分類 10
2-2-1 全局路徑規劃方法 10
2-2-2 局部路徑規劃方法 14
2-3 機器人的環境設定 17
2-3-1 一般論文使用柵欄法的環境設定 17
2-3-2 本篇論文所使用的環境設定 21
第三章  本論文採用之方法與分析 23
3-1 快速隨機搜尋樹演算法(RRT) 24
3-1-1 傳統的快速隨機搜尋樹 24
3-1-2 雙向擴展快速隨機搜尋樹 27
3-2 粒子群演算法(PSO) 28
3-2-1 粒子群演算法的起源及機制原理 28
3-2-2 粒子群演算法的數學描述 29
3-2-3 粒子群演算法的流程 30
3-2-4 粒子群演算法的參數分析 32
3-3 改良式粒子演算法介紹 33
3-3-1 PSO with an aging leader and challengers(ALC-PSO) 34
3-3-2 生命週期機制 35
3-3-3 產生挑戰者機制 37
3-3-4 產生新的領導人的機制 38
3-3-5 詳細的ALC-PSO演算法步驟 39
第四章  改良路徑規劃之方法 42
4-1 改良方法一 43
4-1-1 改良方法一的設計理念 43
4-1-2 適應函數設計與懲罰函數設計 47
4-2 改良方法二 51
4-2-1 延伸點的選擇方式 51
4-2-2 改良方法二的演算法流程 52
4-3 粒子成長暨ALC-PSO改良演算法 57
4-3-1 改良延伸點選擇方式 57
4-3-2 粒子成長暨ALC-PSO演算法流程 62
第五章  實驗結果與討論 67
5-1 各個演算法相關參數設定 67
5-2 粒子成長暨ALC-PSO演算法的細節設定 68
5-2-1 群體大小是否隨著疊代次數改變而減少規劃時間 68
5-2-2 延伸點設定的多寡 70
5-2-3 在產生延伸點之間的疊代次數變化 71
5-3 與其他改良粒子群演算法比較(一般地圖) 74
5-3-1 本文使用之改良粒子演算法與其他改良粒子群演算法之比較 74
5-3-2 本文演算法與其他路徑規劃演算法之比較 75
5-4 加入危險度機制與安全區比較(一般地圖) 78
5-5 與其他改良演算法比較(複雜地圖) 81
5-6 模擬在動態環境中 85
第六章  結論與建議 87
6-1 結論 87
6-2 建議 88
參考文獻 89
文章發表 94
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指導教授 鍾鴻源(Hung-Yuan Chung) 審核日期 2014-8-15
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