§ 瀏覽學位論文書目資料
  
系統識別號 U0002-2909202013063700
DOI 10.6846/TKU.2020.00883
論文名稱(中文) 多無人機最小化充電延遲時間之排程技術
論文名稱(英文) MINIMIZING CHARGING TIME FOR DRONES BASED ON CHARGING VEHICLES
第三語言論文名稱
校院名稱 淡江大學
系所名稱(中文) 資訊工程學系資訊網路與多媒體碩士班
系所名稱(英文) Master's Program in Networking and Multimedia, Department of Computer Science and Information Engine
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) 何振綱
研究生(英文) Jhen-Gang He
學號 607420097
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 35頁
口試委員 指導教授 - 張世豪(shhchang@mail.tku.edu.tw)
委員 - 張志勇(cychang@mail.tku.edu.tw)
委員 - 張世豪(shhchang@mail.tku.edu.tw)
委員 - 廖文華(whliao@gm.ttu.edu.tw)
關鍵字(中) 無人飛行器
無人機充電
最小化任務時間
路徑規劃
關鍵字(英) Unmanned Aerial Vehicle
Drone Charging
Minimizing Mission Time
Path Planning
第三語言關鍵字
學科別分類
中文摘要
傳統無人飛行器延長壽命的技術,大多仰賴固定式充電站進行無人飛行器的電量補給,但是固定式充電站建設成本較高,且在初始規劃階段就必須將固定式充電站的位置以及數量規劃好,但無人飛行器的任務需求日益增加,無人飛行器的數量也隨之增加,無人飛行器需要經常等待空閒的充電站來進行電量補給,這會導致無人飛行器的任務完成時間延後、資料的丟失,以及無人飛行器等待而消耗電量。
本論文假設存在著許多移動充電車,其位置及速度固定且可知,其上搭載著充電設備,可對無人機進行充電,本論文所提出的方法共三步驟,首先,將無人機充電任務的初始任務位置、目標任務位置以及以及充電車的路徑,依單位時間切割為多個狀態位置;其次,將切割好的狀態位置製作成帶有無人機獲得電量的有向圖;而後,將所形成的帶權有向圖轉化為相鄰矩陣,並透過本論文所提出基於動態規劃的MCTCV算法,找出無人機在達到門檻電量並且相對花費時間最小的充電任務路徑。實驗顯示,本論文所提出的充電策略,在充電任務花費時間以及相同時間下所獲得的電量均優於相關研究。
英文摘要
The technology for extending the life of traditional unmanned aerial vehicles mostly relies on fixed charging stations for the power supply of unmanned aerial vehicles. However, the construction cost of fixed charging stations is relatively high, and the location and number of fixed charging stations must be planned in the initial planning stage. However, the mission demand of unmanned aerial vehicles is increasing, and the number of unmanned aerial vehicles is also increasing. Unmanned aerial vehicles often need to wait for idle charging stations to replenish power. This will cause delays in the completion of unmanned aerial vehicles' missions, loss of data, and unmanned aerial vehicles. Wait and consume power.

This paper assumes that there are many mobile charging vehicles whose positions and speeds are fixed and known. They are equipped with charging equipment to charge the drone. The method proposed in this paper has three steps. First, the task of charging the drone The initial mission position, the target mission position, and the path of the charging car are cut into multiple state positions per unit time; secondly, the cut state positions are made into a directed graph with the power obtained by the drone; then, the resulting The weighted directed graph of is transformed into an adjacent matrix, and the MCTCV algorithm based on dynamic programming proposed in this paper is used to find the charging task path that the drone reaches the threshold power and takes the least time. Experiments show that the charging strategy proposed in this paper is better than the related research in the time spent on the charging task and the amount of electricity obtained in the same time.
第三語言摘要
論文目次
目錄
第一章、簡介	1
第二章、相關研究	5
A. 充電排程	5
B. 路徑規劃	6
第三章、系統架構	8
3.1 環境描述	8
3.2 無人機充電任務目標	9
3.3 移動充電車速度不對等	9
第四章、mctcv算法	12
A.	時槽的切割	12
B.	有向圖繪製	13
C.	無人機充電任務的相鄰矩陣	16
D.	無人機充電任務的最大電量	17
E.	無人機充電任務的門檻電量	18
第五章、實驗分析	20
第六章、結論	23
參考文獻	24
附錄:英文論文	26
 
圖目錄
圖 1、充電任務場景、充電車路線無人機初始電量以及目標電量	8
圖 2、無人機透過多台充電車充電	9
圖 3、充電車在速度以及充電效率不對等	10
圖 4、充電車每個單位時間的狀態位置	12
圖 5、無人機充電任務狀態圖	13
圖 6、無人機換乘的所有可能	15
圖 7、無人機達到門檻電量最短時間路徑	17
圖 8、無人機充電任務花費時間	18
圖 9、無人機充電任務	20
圖 10、無人機在每一次的充電任務花費時間	21
圖 11、無人機在每一次的充電任務獲得電量	21

表目錄
表 1:相關研究功能比較表	7
表 2、符號表	10
參考文獻
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J. Chen, T. Ma, and C. Su, “The study of UAV intelligent support mode based on battlefield networks,” in Proc. IEEE 2nd Adv. Inf. Technol., Electron. Autom. Control Conf. 2017, pp. 1912–1915.
V. Hassija, V. Chamola, V. Saxena, D. Jain, P. Goyal, and B. Sikdar, “A survey on IoT security: Application areas, security threats, and solution architectures,” IEEE Access, vol. 7, pp. 82 721–82 743, Jul. 2019.
A. Hanyu, Y. Kawamoto, and N. Kato, “On improving flight energy efficiency in simultaneous transmission and reception of relay using uavs,” in Proc. 15th Int. Wireless Commun. Mobile Computing Conf., Jun. 2019, pp. 967–972.
L. Li, J.Wu, Y. Xu, J. Che, and J. Liang, “Energy-controlled optimization algorithm for rechargeable unmanned aerial vehicle network,” in Proc. 12th IEEE Conf. Ind. Electron. Appl., 2017, pp. 1337–1342.
V. Chamola et al., “An optimal delay aware task assignment scheme for wireless SDN networked edge cloudlets,” Future Gener. Comput. Syst., vol. 102, pp. 862–875, 2020.
Qiang Liu, Miao Li, Jun Yang, Jing Lv, Kai Hwang, M.Shamim Hossain and Ghulam Muhammad, “Joint power and time allocation in energy harvesting of UAV operating system,” Computer Communications 150 (2020) 811-817.
Vikas Hassija, Vikas Saxena, Vinay Chamola, “Scheduling drone charging for multi-drone network based on consensustime-stamp and game theory,” Computer Communications 149 (2020) 51-61.
Vikas Hassija, Vinay Chamola, Dara Nanda Gopala Krishna, Mohsen Guizani, “A distributed framework for energy trading between uavs and charging stations for critical applications,” IEEE Transactions On Vehicular Technology, vol. 69, no. 5, March 2020.
Deguo Sun, Xinying Peng, Rui Qiu, Yidan Huang,“The Traveling Salesman Problem: Route Planning of Recharging Station-Assisted Drone Delivery,” ICMSEM 2020: Proceedings of the Fourteenth International Conference on Management Science and Engineering Management pp 13-23.
Deguo Sun, Xinying Peng, Rui Qiu, Yidan Huang,“The Traveling Salesman Problem: Route Planning of Recharging Station-Assisted Drone Delivery,” IEEE Transactions On Systems, Man, And Cybernetics: Systems, vol. 49, vo. 1, JAN 2019
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