Path Planning for UAV Communication Networks: Related Technologies, Solutions, and Opportunities

被引:15
作者
Luo, Junhai [1 ]
Wang, Zhiyan [1 ]
Xia, Ming [2 ]
Wu, Linyong [2 ]
Tian, Yuxin [1 ]
Chen, Yu [1 ]
机构
[1] Univ Elect Sci & Technol China, Chengdu, Sichuan, Peoples R China
[2] Sichuan Jiuzhou Elect Grp Co Ltd, Jiuzhou, Peoples R China
关键词
Unmanned aerial vehicle communication network; path planning; multi-UAV-assisted path planning; reinforcement learning; UNMANNED AERIAL VEHICLES; JOINT TRAJECTORY DESIGN; THROUGHPUT MAXIMIZATION; SENSOR NETWORKS; LARGE-SCALE; OPTIMIZATION; ENVIRONMENTS; MINIMIZATION; ALLOCATION; ALGORITHM;
D O I
10.1145/3560261
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Path planning has been a hot and challenging field in unmanned aerial vehicles (UAV). With the increasing demand of society and the continuous progress of technologies, UAV communication networks (UAVCN) are also flourishing. The mobility of UAV nodes allows for flexible network deployment, but some challenges are brought, such as power constraints, throughput, cost, and time efficiency. Therefore, path planning is significant for UAVCN. This article presents a review of UAVCN path planning. We first introduce the network structure and performance evaluation of UAVCN. We then investigate the generic UAV path planning algorithms and the path planning algorithms in UAVCN. In this article, the advantages and disadvantages of each path planning algorithm and the functional problems. The challenges faced in path planning for UAVCN, the solutions, state-of-the-art, and representative results are also presented. In addition, we illustrate future research directions for UAVCN path planning as well, which can provide some help to researchers.
引用
收藏
页数:37
相关论文
共 148 条
  • [1] Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges
    Aggarwal, Shubhani
    Kumar, Neeraj
    [J]. COMPUTER COMMUNICATIONS, 2020, 149 : 270 - 299
  • [2] Al-Emadi S., 2020, PROC 3 INT C ADV COM, P1
  • [3] UAV-Assisted Content Delivery in Intelligent Transportation Systems-Joint Trajectory Planning and Cache Management
    Al-Hilo, Ahmed
    Samir, Moataz
    Assi, Chadi
    Sharafeddine, Sanaa
    Ebrahimi, Dariush
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 22 (08) : 5155 - 5167
  • [4] Ali Meerza SyedIrfan., 2019, IEEE 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), P1, DOI DOI 10.1109/ICASERT.2019.8934450
  • [5] Mobile Data Collection from Sensor Networks with Range-Dependent Data Rates
    Annuar, Noralifah
    Bergmann, Neil
    Jurdak, Raja
    Kusy, Branislav
    [J]. 2017 IEEE 42ND CONFERENCE ON LOCAL COMPUTER NETWORKS WORKSHOPS (LCN WORKSHOPS 2017), 2017, : 53 - 60
  • [6] Balan K, 2018, 2018 9TH IEEE ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS & MOBILE COMMUNICATION CONFERENCE (UEMCON), P497, DOI 10.1109/UEMCON.2018.8796810
  • [7] Bayerlein H, 2018, IEEE INT WORK SIGN P, P945
  • [8] Dual-UAV-Enabled Secure Communications: Joint Trajectory Design and User Scheduling
    Cai, Yunlong
    Cui, Fangyu
    Shi, Qingjiang
    Zhao, Minjian
    Li, Geoffrey Ye
    [J]. IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2018, 36 (09) : 1972 - 1985
  • [9] Çalik SK, 2016, 2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU), P1409, DOI 10.1109/SIU.2016.7496013
  • [10] An Energy Efficient Framework for UAV-Assisted Millimeter Wave 5G Heterogeneous Cellular Networks
    Chakareski, Jacob
    Naqvi, Syed
    Mastronarde, Nicholas
    Xu, Jie
    Afghah, Fatemeh
    Razi, Abolfazl
    [J]. IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING, 2019, 3 (01): : 37 - 44