臺灣博碩士論文加值系統

稻米為華人主食，也是臺灣最大面積的栽培作物，臺灣氣候為高溫度及高濕度，在這種環境下導致病蟲害繁衍快速，需要即時進行農藥噴灑，減緩病蟲害的擴散。但隨著台灣進入高齡社會，農牧人口面臨人力缺口與高齡化問題，現今興起了一種的農藥噴灑技術，使用無人機隊執行農藥噴灑作業，但是在執行大面積的農藥噴灑作業時，會遭遇到無人機藥箱需要重新填充農藥與更換無人機電池的情況。因此，該如何規劃每架無人機所負責的噴灑區域、重新填充的農藥量與更換電池排程，這些問題稱為農業田地作業問題(Agricultural Field Operations Problem, AFOP)。
本研究主要探討使用無人機隊執行農藥噴灑作業，並考量無人機的可飛行時間與無人機在田地內噴灑農藥時的路徑限制，使用具容量之車輛途程問題(Capacitated Vehicle Routing Problem, CVRP)的概念進行無人機隊的噴灑作業規劃。以最小化無人機隊中的最長完工時間為目標，時間項目分別為：(1)旅行時間、(2)田間作業時間、(3)重新填充時間、及(4)更換電池時間，綜合上述目標與相關限制，建構數學模型，並以Gurobi 8求解軟體建構混合整數線性規劃求解程式，以驗證數學模型之正確性。
以Mix-opt模擬退火演算法建立本研究之求解演算法，再透過Python程式語言開發求解程式，並以田口方法找出啟發式求解演算法的較佳參數組合。本研究以雲林縣褒忠鄉潮厝村的田地作為實際案例，藉由實際案例求解以驗證啟發式求解演算法之正確性與有效性。最後針對實際案例進行敏感度分析，包含(1)調整無人機噴灑農藥的方向、(2)無人機的使用數量、及(3)無人機的農藥噴灑速率，並針對三種敏感度分析結果彙整成結論，以提供經營者在執行農藥噴灑作業時的規劃參考，以期解決實務上農業作業時的作業規劃問題。

Rice is a staple Chinese food which is the largest crop in Taiwan. It is necessary to spray pesticides immediately for preventing the spread of pests and diseases. Nowadays, the unmanned aerial vehicles (UAVs) are invented to carry out pesticide spraying operations. It is also necessary to consider that the UAVs need to refill the pesticide and change the battery in a large field. This operation can be investigated as an agricultural field operations problem (AFOP) which is the major topic in this study.
This study transforms the AFOP into a capacitated vehicle routing problem (CVRP). The goal of this study is to find an efficient method for arranging UAVs routing, refilling pesticides operation, and schedule of changing the battery. A mixed integer linear program mathematical model is developed to minimize the maximum operating time of UAVs. The overall operation time includes: (1) travel time, (2) operation time, (3) refilling time, and (4) changing battery time.
A mathematical model is developed first. The solution algorithm is then developed by using the logic of Mix-opt simulated annealing algorithm. The solution program using Python language is also developed to solve a real-world problem. The Taguchi method is used to find better parameters used in the solution algorithm. Finally, a sensitivity analysis is conducted including: (1) adjust the UAVs spraying direction, (2) adjust the number of spraying UAVs, and (3) adjust the UAVs spraying rate. Based on the results of sensitivity analysis, suggestions and conclusions are made to provide the management level of agricultural operations.

目錄
摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍與限制 4
1.4 研究架構與流程 4
第二章 文獻探討 6
2.1 農業用無人機概述 6
2.1.1 農業用無人機之現況與未來發展 7
2.1.2 無人機負載計算 10
2.1.3 小結 13
2.2 農業之車輛途程問題 13
2.3 車輛途程問題之求解方法 18
2.3.1 禁忌搜尋演算法 19
2.3.2 遺傳演算法 20
2.3.3 模擬退火演算法 22
2.3.4 人工蜂群演算法 24
2.3.5 小結 25
2.4 田口方法 25
第三章 問題定義與模型建構 28
3.1 問題描述與定義 28
3.2 數學模型建構 37
3.2.1 基本假設 37
3.2.2 變數定義與符號說明 39
3.2.3 數學模型建構 41
3.3 數學模型驗證 45
3.3.1 模型驗證與例題參數設定 45
3.3.2 範例測試求解結果 47
3.4 求解演算法建構 49
3.4.1 建構求解初始途程 50
3.4.2 求解演算法架構 57
3.4.3 產生新途程之建構 61
3.4.3.1 產生新途程架構 61
3.4.3.2 六種產生新途程的機制 65
3.4.3.3 途程修復架構 73
第四章 實際案例探討與結果分析 81
4.1實際案例說明 81
4.2 Mix-opt模擬退火演算法參數測試與設置 85
4.2.1 設定控制因子與水準 85
4.2.2 實驗結果 90
4.2.3 確認性實驗 90
4.3 演算法求解品質驗證 93
4.4 實際案例求解及分析 95
4.4.1 初始解求解結果 95
4.4.2 改善途程求解結果 98
4.4.3 初始解與改善解結果比較 102
4.5 敏感度分析 104
4.5.1 調整無人機農藥噴灑方向 104
4.5.2 調整無人機使用數量 110
4.5.3 調整無人機的農藥噴灑速率 113
第五章 結論與未來研究方向 116
5.1 結論 116
5.2 未來研究方向 117
參考文獻 118
附錄 124
附錄一、模式驗證例題之GUROBI 8求解結果 124
附錄二、演算法求解品質驗證例題之基本資料 125
附錄三、實際案例初始解之無人機隊農藥噴灑途程詳細資料 127
附錄四、實際案例改善解之無人機隊農藥噴灑途程詳細資料 129

表目錄
表2.1無人機計算能量消耗參數 12
表3.1 大疆創新MG-1S無人機參數 33
表3.2 無人機調整前後之各項作業時間 37
表3.3 模型驗證例題之大疆創新MS-1S無人機參數 47
表3.4 模型驗證例題的各節點資料 47
表3.5 模型驗證例題之詳細時間資料 49
表4.1 實際案例之田地詳細資料表 83
表4.2 田地節點之資訊表 84
表4.3 各控制因子水準說明表 88
表4.4 L_27直交表之實驗配置表 89
表4.5 L_27直交表之實驗結果表 91
表4.6 控制因子反應表 92
表4.7 較佳參數組合之確認性實驗結果表 93
表4.8 較佳參數組合表 93
表4.9 各題型之基本資訊表 94
表4.10 各題型Gurobi 8求解及本研究之求解演算法求解比較表 95
表4.11 實際案例初始解之各架無人機作業時間資訊表 98
表4.12 實際案例求解5次之求解結果表 98
表4.13 實際案例改善解之各架無人機作業時間資訊表 101
表4.14 實際案例之初始解與改善解比較表 102
表4.15 實際案例之初始解與改善解改善比率表 103
表4.16 調整無人機農藥噴灑方向之原方案與調整方案比較表 107
表4.17 調整無人機農藥噴灑方向之原方案與調整方案差異百分比率表 108
表4.18 調整無人機使用數量之結果表 111
表4.19 調整無人機之農藥噴灑速率結果表 114

 
圖目錄
圖1.1 臺灣歷年農牧人口數圖 2
圖1.2 研究流程架構圖 5
圖2.1 本研究計算無人機負載所消耗之能量 13
圖2.2 農業田地作業轉換為車輛途程問題示意圖 15
圖2.3 農業之車輛途程問題示意圖 15
圖2.4 農業之車輛途程問題節點編碼示意圖 16
圖3.1 拖拉機最小迴轉半徑示意圖 29
圖3.2 無人機平移動作示意圖 29
圖3.3 田地軌道與倉庫編碼示意圖 30
圖3.4 小範例田地示意圖 33
圖3.5 噴灑田地區域劃分示意圖 34
圖3.6 無人機隊執行農藥噴灑作業甘特圖 35
圖3.7 調整後無人機隊執行農藥噴灑作業甘特圖 36
圖3.9 模型驗證例題之Gurobi 8求解結果圖 48
圖3.10 模型驗證例題之無人機隊執行農藥噴灑作業甘特圖 49
圖3.11 整體求解架構 50
圖3.12 初始途程流程圖 55
圖3.13 規劃各架無人機噴灑區域機制 56
圖3.14 補充作業流程圖 56
圖3.15 Mix-opt模擬退火演算法流程圖 60
圖3.16 無人機平移限制示意圖 62
圖3.17 產生新途程架構圖 65
圖3.18 單一重置機制示意圖 67
圖3.19 單一交換機制示意圖 68
圖3.20 段落選取示意圖 69
圖3.21段落重置機制示意圖 69
圖3.22 段落交換機制示意圖 70
圖3.23 途程間單一重置機制示意圖 71
圖3.24 途程間段落重置機制示意圖 73
圖3.25 途程中插入倉庫機制架構圖 76
圖3.26 途程中移除倉庫機制架構圖 80
圖4.1 實際案例之衛星地圖 82
圖4.2 實際案例之田地示意圖 83
圖4.3 控制因子之S/N比反應圖 92
圖4.4 實際案例初始解之無人機噴灑區域規劃結果示意圖 96
圖4.5 實際案例初始解之無人機隊執行農藥噴灑作業甘特圖 97
圖4.6 實際案例改善解之無人機噴灑區域規劃結果示意圖 99
圖4.7實際案例改善解之無人機隊執行農藥噴灑作業甘特圖 100
圖4.8 Mix-opt模擬退火演算法之求解收斂圖 101
圖4.9 Mix-opt模擬退火演算法之求解溫度變化圖 102
圖4.10 調整農藥噴灑方向後之田地示意圖 105
圖4.11 調整農藥噴灑方向之無人機噴灑區域規劃結果示意圖 106
圖4.12 調整農藥噴灑方向後之無人機隊執行農藥噴灑作業甘特圖 106
圖4.13 調整無人噴灑方向後各項時間之變化趨勢圖 108
圖4.14 調整無人機的農藥噴灑方向後之求解收斂圖 109
圖4.15 調整無人機的農藥噴灑方向後之求解溫度變化圖 109
圖4.16 調整無人機使用數量之變化趨勢圖 111
圖4.17 調整無人機使用數量之各方案完工時間圖 112
圖4.18 調整無人機噴灑速率之變化趨勢圖 115
圖4.19 調整無人機噴灑速率之各方案完工時間圖 115

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