從化石燃料轉向可再生能源對於對抗全球溫室效應和空氣污染至關重要。電動車（EV）將取代傳統車輛，而無人機（UAV）則日益受到重視。預計UAV將主導貨物運輸，並擴展至低空領域的乘客運輸。現有的研究提出了為UAV提供充電的方法，例如可再生能源供應、電池交換和充電站。然而，確保可再生能源的穩定供應面臨不可預測性的挑戰。同時間的充電需求可能導致擁堵和延遲。雖然研究提出了使用移動中的EV，透過無線充電來滿足UAV的緊急充電需求，但目前的方法過於簡化。針對每個UAV量身定制充電選項至關重要,本研究提出了一種綜合方法，結合飛行路徑和充電計劃，以提高個別UAV的充電效率和飛行路徑優化。它結合了數據挖掘、優化和機器學習，通過模擬也展示了該方法在滿足UAV充電需求並優化飛行路徑方面的有效性，已經提出像雷射光束充電和無人機之間充電這樣的無線充電選項，但是UAV數量的增加可能會造成延遲。該綜合方法提供了適應性充電選項，包括電池交換、有線充電和電磁無線充電用於日常任務。在合適的天氣條件下，時間緊迫的任務可以使用無線充電或太陽能。模擬結果顯示，在高峰時段減少了電網負載，滿足了充電需求並降低了成本。這種方法有利於無人機和電網運營商，確保運營效率。所提出的方法使用數據挖掘、最佳化和機器學習，滿足了不同操作者的需求同時優化飛行路徑。通過這些整合，可以實現一個環境友好型的未來。

The shift from fossil fuels to renewables is vital to combat global greenhouse effect and air pollution. Electric vehicles (EVs) will replace traditional vehicles, while unmanned aerial vehicles (UAVs) gain prominence. UAVs are expected to dominate goods delivery and expand to low-altitude airspace for passenger transport. Existing research suggests charging methods like renewable power supply, battery swapping, and charging stations for UAVs. However, ensuring stable power supply from renewables poses challenges due to their unpredictability. Simultaneous charging demands may cause congestion and delays. While studies propose mobile EVs with wireless charging for urgent UAV needs, current approaches oversimplify the process. Customized charging options tailored to each UAV are crucial. This work proposes an integrated approach combining flight path and charging planning for individual UAVs. It incorporates data mining, optimization, and machine learning to enhance charging efficiency and flight path optimization. Simulations demonstrate the effectiveness of this methodology in meeting UAV charging requirements while optimizing flight paths. Wireless charging options like decentralized laser charging and UAV-to-UAV charging have been suggested, but increasing UAV numbers may introduce delays. The integrated approach offers adaptive charging options, including battery swaps, wired charging, and electromagnetic wireless charging for routine missions. Time-critical missions can use wireless charging or solar energy under suitable weather conditions. Simulation results show reduced power grid load during peak hours, meeting charging needs, and minimizing costs. This approach benefits UAV operators, customers, and power grid operators, ensuring efficient operations. Transitioning to renewables and implementing advanced charging mechanisms, along with integrated flight path planning, is crucial for a sustainable future. The proposed approach, using data mining, optimization, and machine learning, caters to diverse operator needs while optimizing flight paths. By integrating these efforts, an environmentally friendly future can be achieved.

誌謝 i
摘要 ii
Abstract iii
Table of Contents iv
List of Figures vi
Chapter 1 Introduction 1
1.1 Motivation 2
1.2 Organization of the dissertation 4
Chapter 2 Literature Review 5
Chapter 3 EV-Assisted Charging Mechanism for UAV 11
3.1 Routing and charging/discharging planning for an EV 14
3.2 Flight Path and Charging Planning for a UAV 20
3.3 Charging and Discharging Management at The Roadside Unit 25
Chapter 4 Flight Path and Charging Mechanism for Internet of UAVs 29
4.1 Preplanning flight paths and charging for UAVs performing Ordinary Mission 34
4.2 Preplanning flight paths and charging for UAVs performing Time-Critical Mission 41
4.3 Real-Time Flight Route and Charging Planning for a UAV on an Ordinary Mission 49
4.4 Real-Time Flight Route and Charging Planning for a UAV on a Time-Critical Mission 52
Chapter 5 Experimental Results 56
5.1 Results of EV-Assisted Charging Mechanism for UAV 56
5.2 Results of Flight Path and Charging Mechanism for Internet of UAVs 62
Chapter 6 Conclusion and Future Work 69
6.1 Conclusion 69
6.2 Future Work 70
References 71

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