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研究生:紀伯聰
研究生(外文):CHI, PO-TSUNG
論文名稱:於動態障礙環境中視角可調無人機之路徑最佳化
論文名稱(外文):Multi-Objective Evolutionary Path Planning in Dynamic Obstacle Environment for Unmanned Aerial Vehicles with Adjustable Imaging Angle
指導教授:陳建宏陳建宏引用關係
指導教授(外文):CHEN, JIANG-HUNG
口試委員:石昭玲陳穎平
口試委員(外文):SHIH, JAU-LINGCHEN, YING-PING
口試日期:2017-06-07
學位類別:碩士
校院名稱:中華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:60
中文關鍵詞:無人機路徑規劃動態障礙多目標遺傳演算法
外文關鍵詞:UAVPath PlanningDynamic ObstacleMulti-objective Genetic Algorithms
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無人飛行器(Unmanned Aerial Vehicle, UAV)起源自軍事用途,但隨著時代的改變與科技的演進,現在已經被廣泛應用於民用領域。除了已有許多民用領域實際應用外,至今有許多新興的應用也嘗試使用無人機執行任務,例如使用無人機進行山林巡視環境監控、使用無人機發送包裹、無人機執行勘災、無人機情蒐監控等等。

在無人機環境監控應用上,因為無人機能到達一般地面交通難以到達的區域,因此可以達到快速監控整個區域的效果;目前也已廣泛利用無人機執行勘災任務,警方也曾利用無人機情蒐山區,並藉此捕獲逃逸藏匿已久的通緝犯,可見使用無人機執行監控任務是相當具有效益的方式。因此無人機路徑規劃便成為無人機自動化中極具挑戰性的主要問題之一。

關於無人機路徑規劃的傳統研究,雖然也有迴避障礙的文獻,但是皆屬於閃避不可飛行之障礙的靜態環境,並且皆是以規劃飛行路徑而非區域監控的角度出發。本研究基於具可轉換視角鏡頭的無人機於區域監控任務的路徑規劃,研究在監控區域內若有動態障礙時,該如何規劃出閃避移動障礙的最佳飛行器路線,並同時達成飛行成本最小、負載平衡、和最大區域監控需求之多目標最佳化。

本研究提出一個多目標最佳化的方式解決此一多目標最佳化問題。本研究提出以六角形座標以基礎的方式計算路徑成本,並採用以線段方式設計的染色體進行編碼,再使用多目標遺傳演算法來求解此問題。本研究以真實鐵路系統模擬動態障礙環境,配置執行軌道與周邊監控任務。經實驗結果顯示,本研究所設計的演算法可以有效的解決於具動態障礙環境的監控任務,且仍可取得具有不錯的解,並同時達成飛行成本最小、負載平衡、和最大區域監控需求之多目標最佳化。

Unmanned Aerial Vehicle (UAV) was originated for the military use. With rapid improvements of UAV technology, UAV has many practical applications in the civilian areas. Because UAV can overcome harsh environmental conditions and the limitations of ground transportation, more and more new missions are taking advantages of UAV, such as mountain patrolling, sending packages, environmental monitoring, disaster investigation, and searching and locating the escaped criminals. As a result, planning paths of UAV has become one of the major task in UAV applications.

In the literature of UAV path planning, most of the approaches are path planning without any obstacles. Only a few approaches proposed path planning considered static obstacles by avoiding the no-fly zone. However, those approaches only focused on path planning but ignored the major goal in regional monitoring. Therefore, this research investigates the application of UAVs with adjustable imaging angle in monitoring a dynamic obstacles environment, considering the objectives of path planning, UAV load balancing, and monitoring requirements.

In this paper, we proposed a multi-objective evolutionary approach to solve the investigated problem. A real-world railway environment is adopted to simulate the dynamic obstacles environment, UAVs are deployed for the orbit and peripheral monitoring tasks. The experimental results show that the proposed approach can effectively obtain multiple non-dominated solutions, which optimized the objectives of path planning, UAV load balancing, and monitoring requirements

摘要 1
ABSTRACT 2
誌謝 3
目錄 4
表目錄 6
圖目錄 7
第一章 緒論 9
1.1 研究背景與動機 9
1.2 問題描述 9
1.3 研究目標 9
1.4 論文架構 10
第二章 文獻探討 11
2.1 無人飛行器 11
2.2 路徑規劃 12
2.3 遺傳演算法 12
2.3.1演算法介紹 12
2.3.2 遺傳演算法的優缺點 14
2.3.3 遺傳演算法的運算 14
2.4 多目標演算法 16
2.5 Hypervolume 18
2.6 六邊形座標系統 18
第三章 無人機路徑規劃問題 21
3.1 環境介紹 21
3.2 監控區域說明 21
3.3 符號說明與目標函式 23
3.3.1 符號說明 23
3.3.2 監控區域說明 24
3.3.3 目標函式說明 24
第四章 設計多目標遺傳演算法求解具動態障礙之路徑規劃問題 26
4.1 演算法流程 26
4.2 地圖設計 27
4.3 染色體編碼 27
4.4 適應度計算 28
4.5 選擇 28
4.6 交配 28
4.7 突變 29
4.8 動態障礙的迴避 30
4.9 修補機制 31
4.10 μ+λ進化策略 31
第五章 實驗及結果 32
5.1 實驗參數設定 32
5.1.1 實驗一 33
5.1.2 實驗二 33
5.1.3 實驗三 33
5.1.4 實驗四 34
5.1.5 實驗五 34
5.2 實驗結果分析 35
5.2.1 實驗一 35
5.2.2 實驗二 39
5.2.3 實驗三 43
5.2.4 實驗四 47
5.2.5 實驗五 50
5.2.6 無人機飛行路徑 52
第六章 結論 57
參考文獻 58


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