§ 瀏覽學位論文書目資料
  
系統識別號 U0002-2909202009023600
DOI 10.6846/TKU.2020.00880
論文名稱(中文) 最小化抵達時間之多無人機充電策略研究
論文名稱(英文) Research on UAV charging strategy to minimize the time of arrival
第三語言論文名稱
校院名稱 淡江大學
系所名稱(中文) 資訊工程學系碩士班
系所名稱(英文) Department of Computer Science and Information Engineering
外國學位學校名稱
外國學位學院名稱
外國學位研究所名稱
學年度 108
學期 2
出版年 109
研究生(中文) 魏文湧
研究生(英文) Wen-Yung Wei
學號 608410063
學位類別 碩士
語言別 繁體中文
第二語言別 英文
口試日期 2020-07-10
論文頁數 31頁
口試委員 指導教授 - 陳伯榮
委員 - 廖文華
委員 - 蘇民揚
關鍵字(中) 無人機
電池充電
路徑排程規劃
關鍵字(英) Drone
Charging batteries
Route planning
第三語言關鍵字
學科別分類
中文摘要
無人機是一種通過無線控制或預設好的路徑來進行控制,具有攜帶多種功能設備執行各種任務的能力,並且能夠多次利用無人機,因結構簡單、尺寸小、造價低、生存率高、使用靈活、適應力強、零傷亡等等優勢,而被廣泛應用在各種領域。但是,由於蓄電池技術和傳統供電技術的限制,使無人機續航力、靈活性、便利性等方面的發展受到了限制,如何靈活、便利地為無人機補充電力無人機的發展過程中一個重要的發展方向和需要解決的問題。為了解決這個問題,本論文將無線充電傳輸技術應用到無人機充電技術上,提出基於無線充電傳輸模式的無人機無線充電技術,並擬設計多台充電車對無人機進行充電與選擇最佳行走路徑的方法。
本研究的環境是多台無人機對少台充電站,在完成同一個任務中,每台無人機都會有自己的出發位置與目的地位置,而充電站的位置是固定。在這個環境中,可以透過 5G 將數據上傳到雲端進行計算。本論文主要解決的目標是縮短無人機在任務中的所花費的時間,進而縮短全局的任務時間。我們所提出的主要方法包含先進行運算無人機是否有足夠電量可以到達充電站進行充電,再進行無人機路徑規劃階段。因此,透過本論文的演算法可以達到在有限的區域內花費的時間最少,以便快速完成任務,達到最短時間的完成目標。
英文摘要
UAV is a kind of wireless control or preset path to ontrol, with the ability to carry a variety of functional equipment to carry out a variety of tasks, and can be used repeatedly. Because of the advantages of simple structure, small size, low cost, high survival rate, flexible use, strong adjustment, zero casualties and other advantages, UAV is widely used in various fields. However, due to the battery technology With the limitation of traditional power supply technology, the development of UAV's endurance, flexibility and convenience is limited. How to supplement power for UAV flexibly and conveniently is an important development direction and problem to be solved in the development process of UAV. In order to solve this problem, this paper applies wireless charging transmission echnology to UAV charging technology, and proposes a solution Based on the wireless charging transmission mode of UAV wireless charging technology, this paper proposes to design the method of charging UAV with multiple charging vehicles and selecting the best walking path. The environment of this study is multiple UAVs to a few charging stations. In the same task, each UAV will have its own starting position and destination location, and the location of charging station is fixed. In this environment, the data can be uploaded to the cloud through 5g for calculation. The main goal of this paper is to shorten the time spent by UAVs in the task and improve the efficiency of UAV In order to shorten the global task time, the main method proposed in this paper includes first calculating whether the UAV has enough power to reach the charging station for charging, and then the UAV path planning stage. Therefore, the algorithm of this paper can achieve the minimum time spent in the limited area, so as to complete the task quickly and achieve the goal of the shortest time.
第三語言摘要
論文目次
目錄
目錄	V
圖目錄	VI
表目錄	VII
第一章、簡介	1
第二章、相關研究	4
第三章、系統架構	7
3.1 本論文使用的環境	7
3.2 問題分析	8
3.3 最小化抵達時間之多無人機充電策略的演算法	9
第四章、模擬實驗	17
第五章、結論	20
參考文獻	21
附錄-英文論文	22
 
圖目錄
圖 1、無人機環境	12
圖 2、無人機選擇充電站	15
圖 3、充電站使用率	18
圖 4、全局完成時間	19

 
表目錄
表 1 與相關文獻比較	6
表 2符號總表	8
表 3無人機選擇時間總表	13
表 4無人機選擇演算法	14
參考文獻
[1] J. Irizarry, M. Gheisari, and B. N. Walker, “Usability assessment of drone technology
as safety inspection tools,” J. Inf. Technol. Construction, vol. 17, pp. 194–212, Sep.
2012.
[2] A. B. Junaid, A. Konoiko, Y. Zweiri, M. N. Sahinkaya, and L. Seneviratne,
“Autonomous Wireless Self-Charging for Multi-Rotor Unmanned Aerial Vehicles,”
Energies, vol. 10, no. 6, p. 803, Jun. 2017.
[3] Katsuya Fujii and Keita Higuchi and Jun Rekimoto, “Endless Flyer: A Continuous
Flying Drone with Automatic Battery Replacement,” in Ubiquitous Intelligence and
Computing, 2013 IEEE 10th International Conference on and 10th International
Conference on Autonomic and Trusted Computing (UIC/ATC). IEEE, 2013.
[4] T. Toksoz, J. Redding, M. Michini, B. Michini, J. P. How, M. A. Vavrina, and J. Vian,
“Automated Battery Swap and Recharge to Enable Persistent UAV Missions,” in
American Institute of Aeronautics and Astronautics, 2011.
[5] Q. Zhu, H. Han, J. Yu, and Z. Du, “Multi-objective optimal scheduling of uav
resources for emergency mapping,” Wuhan University (Information Science Edition),
vol. 42, no. 11, pp. 1608–1615, 2017.
[6] J. Kim, S. Kim, J. Jeong, H. Kim, J. Park and T. Kim, "CBDN: Cloud-Based Drone
Navigation for Efficient Battery Charging in Drone Networks," in IEEE Transactions
on Intelligent Transportation Systems, vol. 20, no. 11, pp. 4174-4191, Nov. 2019, doi:
10.1109/TITS.2018.2883058.
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