survey

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

Authors:

Yu ChenAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 9

Article No.: 193, Pages 1 - 37

https://doi.org/10.1145/3560261

Published: 16 January 2023 Publication History

Abstract

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.

References

[1]

R. Reshma, Tirumale K. Ramesh, and Praveen Kumar. 2015. Security incident management in ground transportation system using UAVs. In Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 1–7.

[2]

Ahmed Al-Hilo, Moataz Samir, Chadi Assi, Sanaa Sharafeddine, and Dariush Ebrahimi. 2021. UAV-assisted content delivery in intelligent transportation systems-joint trajectory planning and cache management. IEEE Trans. Intell. Transport. Syst. 22, 8 (2021), 5155–5167.

Digital Library

[3]

Dariush Ebrahimi, Sanaa Sharafeddine, Pin-Han Ho, and Chadi Assi. 2019. UAV-aided projection-based compressive data gathering in wireless sensor networks. IEEE Internet Things J. 6, 2 (2019), 1893–1905.

[4]

Zendai Kashino, Goldie Nejat, and Beno Benhabib. 2019. Multi-UAV-based autonomous wilderness search and rescue using target iso-probability curves. In Proceedings of the International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 636–643.

[5]

Praveen K. R. Maddikunta, Saqib Hakak, Mamoun Alazab, Sweta Bhattacharya, Thippa R. Gadekallu, Wazir Z. Khan, and Quoc-Viet Pham. 2021. Unmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges. IEEE Sensors J. 21, 16 (2021), 17608–17619.

[6]

Shuyan Hu, Wei Ni, Xin Wang, Abbas Jamalipour, and Dean Ta. 2021. Joint optimization of trajectory, propulsion, and thrust powers for covert UAV-on-UAV video tracking and surveillance. IEEE Trans. Inf. Forens. Secur. 16 (2021), 1959–1972.

Digital Library

[7]

Xiaonan Liu, Zan Li, Nan Zhao, Weixiao Meng, Guan Gui, Yunfei Chen, and Fumiyuki Adachi. 2019. Transceiver design and multihop D2D for UAV IoT coverage in disasters. IEEE Internet Things J. 6, 2 (2019), 1803–1815.

[8]

Dezhi Chen, Qi Qi, Zirui Zhuang, Jingyu Wang, Jianxin Liao, and Zhu Han. 2021. Mean field deep reinforcement learning for fair and efficient UAV control. IEEE Internet Things J. 8, 2 (2021), 813–828.

[9]

Sandeep K. Singh, Kamal Agrawal, Keshav Singh, Chih-Peng Li, and Wan-Jen Huang. 2020. On UAV selection and position-based throughput maximization in multi-UAV relaying networks. IEEE Access 8 (2020), 144039–144050.

[10]

Xiaobin Xu, Hui Zhao, Haipeng Yao, and Shangguang Wang. 2021. A blockchain-enabled energy-efficient data collection system for UAV-assisted IoT. IEEE Internet Things J. 8, 4 (2021), 2431–2443.

[11]

Aziz A. Khuwaja, Yunfei Chen, Nan Zhao, Mohamed-Slim Alouini, and Paul Dobbins. 2018. A survey of channel modeling for UAV communications. IEEE Commun. Surv. Tutor. 20, 4 (2018), 2804–2821.

Digital Library

[12]

Aziz Altaf Khuwaja, Yunfei Chen, Nan Zhao, et al. 2018. A survey of channel modeling for UAV communications. IEEE Commun. Surv. Tutor. 20, 4 (2018), 2804–2821.

Digital Library

[13]

Aicha Idriss, Hentati, and Lamia ChaariFourati. 2020. Comprehensive survey of UAVs communication networks. J. Netw. Comput. Appl. 72 (2020), 2020:102739.

[14]

Debashisha Mishra and Enrico Natalizio. 2020. A survey on cellular-connected UAVs: Design challenges, enabling 5G/B5G innovations, and experimental advancements. Comput. Netw. 182 (2020).

[15]

Yongs Zeng, Qingqing Wu, and Rui Zhang. 2019. Accessing from the Sky: A tutorial on UAV communications for 5G and beyond. Proc. IEEE 107, 12 (2019), 2327–2375.

[16]

Samira Hayat, Evşen Yanmaz, and Raheeb Muzaffar. 2016. Survey on unmanned aerial vehicle networks for civil applications: A communications viewpoint. IEEE Commun. Surv. Tutor. 18, 4 (2016), 2624–2661.

Digital Library

[17]

Mbazingwa E. Mkiramweni, Chungang Yang, Jiandong Li, and Wei Zhang. 2019. A survey of game theory in unmanned aerial vehicles communications. IEEE Commun. Surv. Tutor. 21, 4 (2019), 3386–3416.

Digital Library

[18]

Azade Fotouhi, Haoran Qing, Ming Ding, Mahbub Hassan, Lorenzo G. Giordano, Adrian Garcia-Rodriguez, and Jinhong Yuan. 2019. Survey on UAV cellular communications: Practical aspects, standardization advancements, regulation, and security challenges. IEEE Commun. Surv. Tutor. 21, 4 (2019), 3417–3442.

Digital Library

[19]

Sara Al-Emadi and Aisha Al-Mohannadi. 2020. Towards enhancement of network communication architectures and routing protocols for FANETs: A survey. In Proceedings of the 3rd International Conference on Advanced Communication Technologies and Networking (CommNet), IEEE. 1–10.

[20]

Zaib Ullah, Fadi Al-Turjman, and Leonardo Mostarda. 2020. Cognition in UAV-aided 5G. Beyond communications: A survey. IEEE Trans. Cog. Commun. Netw. 6, 3 (2020), 872–891.

[21]

Shubhani Aggarwal and Neeraj Kumar. 2020. Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Computer Commun. 149 (2020), 270–299.

Digital Library

[22]

Zakria Qadir, Fahim Ullah, and Hafiz S. Munawar. 2021. Addressing disasters in smart cities through UAVs path planning and 5G communications: A systematic review. Comput. Commun. 168 (2021), 114–135.

[23]

Baoye Song, Gaoru Qi, and Lin Xu. 2019. A survey of three-dimensional flight path planning for unmanned aerial vehicle. In Proceedings of the Chinese Control and Decision Conference (CCDC). 5010–5015.

[24]

Yijing Zhao, Zheng Zheng, and Yang Liu. 2018. Survey on computational-intelligence-based UAV path planning. Knowl.-based Syst. 158, 15 (2018), 54–64.

Digital Library

[25]

Sulaiman S. K. Debnath, Rosil Omar, and Nor B. Latip. 2018. A review on energy efficient path planning algorithms for unmanned air vehicles. Computat. Sci. Technol. 481 (2018), 523–532.

[26]

Yongs Zeng, Qingqing Wu, and Rui Zhang. 2019. Accessing from the sky: A tutorial on UAV communications for 5G and beyond. Proc. IEEE 107, 12 (2019), 2327–2375.

[27]

Haichao Wang, Jinlong Wang, Guoru Ding, Jin Chen, and Jian Yang. 2020. Completion time minimization for turning angle-constrained UAV-to-UAV communications. IEEE Trans. Vehic. Technol. 69, 4 (2020), 4569–4574.

[28]

Jacob Chakareski, Syed Naqvi, Nicholas Mastronarde, Jie Xu, Fatemeh Afghah, and Abolfazl Razi. 2019. An energy-efficient framework for UAV-assisted millimeter wave 5G heterogeneous cellular networks. IEEE Trans. Green Commun. Netw. 3, 1 (2019), 37–44.

[29]

Bertold Van Der Bergh, Alessandro Chiumento, and Sofie Pollin. 2019. LTE in the sky: Trading off propagation benefits with interference costs for aerial nodes. IEEE Commun. Mag. 54, 5 (2019), 44–50.

Digital Library

[30]

Zhe Zhang, Jian Wu, Jiyang Dai, and Cheng He. 2020. A novel real-time penetration path planning algorithm for stealth UAV in 3D complex dynamic environment. IEEE Access 8 (2020), 122757–122771.

[31]

Atif L. Chaudhry, K. Misovec, and Raffaello D'Andrea. 2004. Low observability path planning for an unmanned air vehicle using mixed-integer linear programming. In Proceedings of the 43rd IEEE Conference on Decision and Control (CDC). IEEE, 3823–3829.

[32]

Timur Karatas and Francesco Bullo. 2001. Randomized searches and nonlinear programming in trajectory planning. In Proceedings of the 40th IEEE Conference on Decision and Control. IEEE, 5032–5037.

[33]

Adel Mokrane, Amal C. Braham, and Brahim Cherki. 2020. UAV path planning based on dynamic programming algorithm on photogrammetric DEMs. In Proceedings of the International Conference on Electrical Engineering (ICEE). IEEE, 1–5.

[34]

Shankarachary Ragi and Edwin K. P. Chong. 2013. UAV path planning in a dynamic environment via partially observable Markov decision process. IEEE Trans. Aerosp. Electron. Syst. 49, 4 (2013), 2397–2412.

[35]

Hongyan Shi, Xiaoming Sun, Changzhi Sun, Dongyang Chen, and Yuejun An. 2006. Research of the path planning complexity for autonomous mobile robot under dynamic environments. In Proceedings of the 6th International Conference on Intelligent Systems Design and Applications. IEEE, 216–219.

Digital Library

[36]

Mohammad T. Ramezanlou, Vahid Azimirad, and Manizhe Zakeri. 2019. Hybrid path planning of robots through optimal control and PSO algorithm. In Proceedings of the 7th International Conference on Robotics and Mechatronics (ICRoM). IEEE, 259–264.

[37]

Qiang Wang, An Zhang, and Hai Y. Sun. 2014. MPC and SADE for UAV real-time path planning in 3D environment. In Proceedings of the IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). IEEE, 130–133.

[38]

Kyunghoon Cho, Yunho Choi, and Songhwai Oh. 2017. Reactive controller synthesis for UAV mission planning. In Proceedings of the 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE, 238–241.

[39]

Supriya Jose and Anil Antony. 2016. Mobile robot remote path planning and motion control in a maze environment. In Proceedings of the IEEE International Conference on Engineering and Technology (ICETECH). IEEE, 207–209.

[40]

Vigneshwaran Palanisamy and Senthooran Vijayanathan. 2020. Cluster-based multi-agent system for breadth-first search. In Proceedings of the 20th International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 54–58.

[41]

Li Wenzheng, Liu Junjun, and Yao Shunli. 2019. An improved Dijkstra's algorithm for shortest path planning on 2D grid maps. In Proceedings of the IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC). IEEE, 438–441.

[42]

ZeFang He and Long Zhao. 2017. The comparison of four UAV path planning algorithms based on geometry search algorithm. In Proceedings of the 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 33–36.

[43]

Lydia E. Kavraki, Mihail. N. Kolountzakis, and Jean-Claude Latombe. 1998. Analysis of probabilistic roadmaps for path planning. IEEE Trans. Robot. Autom. 14, 1 (1998), 166–171.

[44]

Chelsea Lau and Katie Byl. 2015. Smooth RRT-connect: An extension of RRT-connect for practical use in robots. In Proceedings of the IEEE International Conference on Technologies for Practical Robot Applications (TePRA). IEEE, 1–7.

[45]

Rongxin Cui, Yang Li, and Weisheng Yan. 2016. Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT*. IEEE Trans. Syst., Man Cybern.: Syst. 46, 7 (2016), 993–1004.

[46]

Reza Mashayekhi, Mohd Y. I. Idris, Mohammad H. Anisi, and Ismail Ahmedy. 2020. Hybrid RRT: A semi-dual-tree RRT-based motion planner. IEEE Access 8 (2020), 18658–18668.

[47]

Chen Zheyi and Xu Bing. 2021. AGV path planning based on improved artificial potential field method. In Proceedings of the IEEE International Conference on Power Electronics, Computer Applications (ICPECA). IEEE, 32–37.

[48]

Herath M. Jayaweera and Samer Hanoun. 2020. A dynamic artificial potential field (D-APF) UAV path planning technique for following ground moving targets. IEEE Access 8 (2020), 192760–192776.

[49]

Varun Kumar Ojhaa, Ajith Abrahamb, and Václav Snášela. 2017. Metaheuristic design of feedforward neural networks: A review of two decades of research. Eng. Applic. Artif. Intell. 60 (2017), 97–116.

Digital Library

[50]

Zhichao Sun, Junjie Wu, Jianyu Yang, et al. 2016. Path planning for GEO-UAV bistatic SAR using constrained adaptive multiobjective differential evolution. IEEE Trans. Geosci. Remote Sens. 54, 11 (2016), 6444–6457.

[51]

Lin Li, Qun Gu, and Li Liu. 2020. Research on path planning algorithm for multi-UAV maritime targets search based on genetic algorithm. In Proceedings of the IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). IEEE, 840–843.

[52]

Guangqiang Li, Qi Liu, Yawei Yang, Fengqiang Zhao, Yiran Zhou, and Chen Guo. 2017. An improved differential evolution-based artificial fish swarm algorithm and its application to AGV path planning problems. In Proceedings of the 36th Chinese Control Conference (CCC). IEEE, 2556–2561.

[53]

Govind P. Gupta, Vrajesh K. Chawra, and Seema Dewangan. 2019. Optimal path planning for UAV using NSGA-II based metaheuristic for sensor data gathering application in wireless sensor networks. In Proceedings of the IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). IEEE, 1–5.

Digital Library

[54]

Zhenyu Zhou, Junhao Feng, Bo Gu, Bo Ai, Shahid Mumtaz, Jonathan Rodriguez, and Mohsen Guizani. 2018. When mobile crowd sensing meets UAV: Energy-efficient task assignment and route planning. IEEE Trans. Commun. 66, 11 (2018), 5526–5538.

[55]

Mohamed Elhoseny, Alaa Tharwat, and Aboul E. Hassanien. 2018. Bezier curve based path planning in a dynamic field using modified genetic algorithm. J. Computat. Sci. 25 (2018), 339–350.

[56]

Soheila Ghambari, Mahmoud Golabi, Julien Lepagnot, Mathieu Brévilliers, Laetitia Jourdan, and Lhassane Idoumghar. 2020. An enhanced NSGA-II for multiobjective UAV path planning in urban environments. In Proceedings of the IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 106–111.

[57]

Fuguang Ding, Zhaoqing Zhang, Mingyu Fu, Yuanhui Wang, and Chenglong Wang. 2018. Energy-efficient path planning and control approach of USV based on particle swarm optimization. In Proceedings of the OCEANS MTS/IEEE Conference. IEEE, 1–6.

[58]

Yi Jiang, Lu Zhang, and Jinhu Liu. 2019. The path planning of mobile sink based on wolf pack algorithm. In Proceedings of the International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). IEEE, 147–150.

[59]

Guozun Tian, Lan Zhang, Xin Bai, and Bing Wang. 2018. Real-time dynamic track planning of Multi-UAV formation based on improved artificial bee colony algorithm. In Proceedings of the 37th Chinese Control Conference (CCC). IEEE, 10055–10060.

[60]

Seda K. Çalık. 2016. UAV path planning with multiagent ant colony system approach. In Proceedings of the 24th Signal Processing and Communication Application Conference (SIU). IEEE, 1409–1412.

[61]

Vincent Roberge, Mohammed Tarbouchi, and Gilles Labonte. 2013. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Industr. Inform. 9, 1 (2013), 132–141.

[62]

Seyedali Mirjalili, Seyed Mohammad Mirjalili, and Andrew Lewis. 2014. Grey Wolf optimizer. Adv. Eng. Softw. 69, 3 (2014) 46–61.

Digital Library

[63]

Chengfang Wu, Xiaoyan Huang, Yuanlin Luo, and Supeng Leng. 2020. An improved fast convergent artificial bee colony algorithm for unmanned aerial vehicle path planning battlefield environment. In Proceedings of the IEEE 16th International Conference on Control & Automation (ICCA). IEEE, 360–365.

[64]

Yuan Zhongrui, Yu Houyu, and Huang Miaohua. 2017. Improved ant colony optimization algorithm for intelligent vehicle path planning. In Proceedings of the International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII). IEEE, 1–4.

[65]

Zhibin Nie, Xiaobing Yang, Shihong Gao, Yan Zheng, Jianhui Wang, and Zhanshan Wang. 2016. Research on autonomous moving robot path planning based on improved particle swarm optimization. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC). IEEE, 2532–2536.

Digital Library

[66]

Karthika Balan and Chaomin Luo. 2018. Optimal trajectory planning for multiple waypoint path planning using tabu search. In Proceedings of the 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 497–501.

[67]

Bing Ma, Ye He, Jiayi Du, and Mengyao Han. 2019. Research on path planning problem of optical fiber transmission network based on simulated annealing algorithm. In Proceedings of the IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 1298–1301.

[68]

Guangjie Han, Chenyu Zhang, Jinfang Jiang, Xuan Yang, and Mohsen Guizani. 2016. Mobile anchor nodes path planning algorithms using network-density-based clustering in wireless sensor networks. J. Netw. Comput. Applic. 85 (2016), 64–75.

Digital Library

[69]

Seyed M. J. Jalali, Abbas Khosravi, Parham M. Kebria, Rachid Hedjam, and Saeid Nahavandi. 2019. Autonomous robot navigation system using the evolutionary multi-verse optimizer algorithm. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 1221–1226

Digital Library

[70]

Jinkai Yin and Weiping Fu. 2018. A hybrid path planning algorithm based on simulated annealing particle swarm for the self-driving car. In Proceedings of the International Computers, Signals and Systems Conference (ICOMSSC). IEEE, 696–700.

[71]

Xiu Yue and Wei Zhang. 2018. UAV Path planning based on K-means algorithm and simulated annealing algorithm. In Proceedings of the 37th Chinese Control Conference (CCC). IEEE, 2290–2295.

[72]

Puneet Kumar, Sahil Garg, Amritpal Singh, Shalini Batra, Neeraj Kumar, and Ilsun You. 2018. MVO-based 2-D path planning scheme for providing quality of service in UAV environment. IEEE Internet Things J. 5, 3 (2018), 1698–1707.

[73]

Yibing Li, Xianzhen Meng, Fang Ye, Tao Jiang, and Yingsong Li. 2020. Path planning based on clustering and improved ACO in UAV-assisted wireless sensor network. In Proceedings of the IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium). IEEE, 57–58.

[74]

HoWon Kim and WonChang Lee. 2021. Real-Time path planning through Q-learning's exploration strategy adjustment. In Proceedings of the International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 1–3.

[75]

Syed I. A. Meerza, Moinul Islam, and Md M. Uzzal. 2019. Q-learning based particle swarm optimization algorithm for optimal path planning of swarm of mobile robots. In Proceedings of the 1st International Conference on Advances in Science, Engineering, and Robotics Technology (ICASERT). IEEE, 1–5.

[76]

Hongji Huang, Yuchun. Yang, Hong Wang, Zhigao Ding, Hikmet Sari, and Fumiyuki Adachi. 2020. Deep reinforcement learning for UAV navigation through massive MIMO technique. IEEE Trans. Vehic. Technol. 69, 1 (2020), 1117–1121.

[77]

Chao Wang, Jian Wang, Yuan Shen, and Xudong Zhang. 2019. Autonomous navigation of UAVs in large-scale complex environments: A deep reinforcement learning approach. IEEE Trans. Vehic. Technol. 68, 3 (2019), 2124–2136.

[78]

Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. Comput. Ence. (2015).

[79]

Nicolas Heess, Dhruva T. B., Srinivasan Sriram, Srinivasan Sriram, Jay Lemmon, and David Silver. 2017. Emergence of locomotion behaviours in rich environments.

[80]

Chenyang Xi and Xinfu Liu. 2020. Unmanned aerial vehicle trajectory planning via staged reinforcement learning. In Proceedings of the International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 246–255.

[81]

S. B. Jasna, P. Supriya, and T. N. P. Nambiar. 2017. Application of game theory in path planning of multiple robots. In Proceedings of the International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 147–151.

[82]

Ines Khoufi, Pascale Minet, and Nadjib Achir. 2016. Unmanned aerial vehicles path planning for area monitoring. In Proceedings of the International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN). IEEE, 1–5.

[83]

Hamid Shiri, Jihong Park, and Mehdi Bennis. 2020. Remote UAV online path planning via neural network-based opportunistic control. IEEE Wirel. Commun. Lett. 9, 6 (2020), 861–865.

[84]

S. A. Gautam and Nilmani Verma. 2014. Path planning for unmanned aerial vehicle based on genetic algorithm & artificial neural network in 3D. In Proceedings of the International Conference on Data Mining and Intelligent Computing (ICDMIC). IEEE, 1–5.

[85]

Jiankun Wang, Wenzheng Chi, Chenming Li, Chaoqun Wang, and Max Q.-H. Meng. 2020. Neural RRT*: Learning-based optimal path planning. IEEE Trans. Autom. Sci. Eng. 17, 4 (2020), 1748–1758.

[86]

Ronglei Xie, Zhijun Meng, Lifeng Wang, Haochen Li, Kaipeng Wang, and Zhe Wu. 2021. Unmanned aerial vehicle path planning algorithm based on deep reinforcement learning in large-scale and dynamic environments. IEEE Access 9 (2021), 24884–24900.

[87]

Yong Zeng, Rui Zhang, and Teng Joon Lim. 2016. Throughput maximization for UAV-Enabled mobile relaying systems. IEEE Trans. Commun. 64, 12 (2016), 4983–4996.

[88]

Lin Shi, Zhongyi Jiang, and Shoukun Xu. 2021. Throughput-aware path planning for UAVs in D2D 5G networks. Ad Hoc Netw. 116, 12 (2021), 102427.

[89]

Lin Shi and Shoukun Xu. 2020. UAV path planning with QoS constraint in device-to-device 5G networks using particle swarm optimization. IEEE Access 8 (2020), 137884–137896.

[90]

Huimin Hu, Ke Xiong, Gang Qu, et al. 2021. AoI-minimal trajectory planning and data collection in UAV-assisted wireless powered IoT networks. IEEE Internet Things J. 8, 2 (2021), 1211–1223.

[91]

Yibing Li, Xianzhen Meng, Fang Ye, et al. 2020. Path planning based on clustering and improved ACO in UAV-assisted wireless sensor network. In Proceedings of the IEEE USNC-CNC-URSI. North American. Radio. Sci. Meeting (Joint with AP-S Symposium). IEEE, 57–58.

[92]

Chunyue Wu, Ruifeng Liang, Feng Xu F, and Feng Ke. 2021. UAV path planning for backscatter communication with phase cancellation. Comput. Commun. 179, 2 (2021), 242–250.

Digital Library

[93]

Jiequ Ji, Kun Zhu, Dusit Niyato, and Ran Wang. 2021. Joint trajectory design and resource allocation for secure transmission in cache-enabled UAV-relaying networks with D2D communications. IEEE Internet Things J. 8, 3 (2021), 1557–1571.

[94]

Yong Zeng, Rui Zhang, and Teng Joon Lim. 2016. Throughput maximization for UAV-enabled mobile relaying systems. IEEE Trans. Commun. 64, 12 (2016), 4983–4996.

[95]

Meng Hua, Luxi Yang, A. Lee Swindlehurst, and Qingqing Wu. 2020. 3D UAV trajectory and communication design for simultaneous uplink and downlink transmission. IEEE Trans. Commun. 68, 9 (2020), 5908–5923.

[96]

Guangchi Zhang, Haiqiang Yan, Yong Zeng, et al. 2018. Trajectory optimization and power allocation for multi-hop UAV relaying communications. IEEE Access 6 (2018), 48566–48576.

[97]

Jie Hu, Xingpeng Cai, and Kun Yang. 2020. Joint trajectory and scheduling design for UAV aided secure backscatter communications. IEEE Wirel. Commun. Lett. 9, 12 (2020), 2168–2172.

[98]

Gang Yang, Rao Dai, and Ying-Chang Liang. 2021. Energy-efficient UAV backscatter communication with joint trajectory design and resource optimization. IEEE Trans. Wirel. Commun. 20, 2 (2021), 926–941.

Digital Library

[99]

Yang Wu, Weiwei Yang, Xinrong Guan. 2020. UAV-UAV communication under malicious jamming: Trajectory optimization with turning angle constraint. In Proceedings of the International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 26–31.

[100]

Zhengrui Huang, Chongcheng Chen, and Miaoxin Pan. 2020. Multiobjective UAV path planning for emergency information collection and transmission. IEEE Internet Things J. 7, 8 (2020), 6993–7009.

[101]

Wanmei Feng, Nan Zhao, Shaopeng Ao, et al. 2020. Joint 3D trajectory design and time allocation for UAV-enabled wireless power transfer networks. IEEE Trans. Vehic. Technol. 69, 9 (2020), 9265–9278.

[102]

Samir Si-Mohammed, Adlen Ksentini, Maha Bouaziz, et al. 2020. UAV mission optimization in 5G: On reducing MEC service relocation. In Proceedings of the IEEE Global Communications Conference. IEEE, 1–6.

Digital Library

[103]

Yunlong Cai, Fangyu Cui, Qingjiang Shi, and Minjian Zhao. 2018. Dual-UAV-enabled secure communications: Joint trajectory design and user scheduling. IEEE J. Select. Areas Commun. 36, 9 (2018), 1972–1985.

Digital Library

[104]

Dongfang Xu, Yan Sun, Derrick Wing Kwan Ng, et al. 2020. Multiuser MISO UAV communications in uncertain environments with no-fly zones: Robust trajectory and resource allocation design. IEEE Trans. Commun. 68, 5 (2020), 3153–3172.

[105]

Lihong Liu, Bailing Tian, Xinyi Zhao, and Qun Zong. 2019. UAV autonomous trajectory planning in target tracking tasks via a DQN approach. In Proceedings of the IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 277–282.

[106]

Ursula Challita, Walid Saad, and Christian Bettstetter. 2019. Interference management for cellular-connected UAVs: A deep reinforcement learning approach. IEEE Trans. Wirel. Commun. 18, 4 (2019), 2125–2140.

[107]

Harald Bayerlein, Paul De Kerret, and David Gesber. 2018. Trajectory optimization for autonomous flying base station via reinforcement learning. In Proceedings of the IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 1–5.

[108]

Yu Lin, Tianyu Wang, and Shaowei Wang. 2019. UAV-Assisted emergency communications: An extended multi-armed bandit perspective. IEEE Commun. Lett. 23, 5 (2019), 938–941.

[109]

Tiankui Zhang, Jiayi Lei, Yuanwei Liu, et al. 2021. Trajectory optimization for UAV emergency communication with limited user equipment energy: A safe-DQN approach. IEEE Trans. Green Commun. Netw. 5, 3 (2021), 1236–1247.

[110]

Zhihan Huang and Xiaodong Xu. 2021. DQN-based relay deployment and trajectory planning in consensus-based multi-UAVs tracking network. In Proceedings of the IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 1–7.

[111]

Hang Qi, Zhiqun Hu, Hao Huang, et al. 2020. Energy efficient 3-D UAV control for persistent communication service and fairness: A deep reinforcement learning approach. IEEE Access 8 (2020), 53172–53184.

[112]

Zhonghao Lyu, Chenhao Ren, and Ling Qiu. 2020. Movement and communication co-design in multi-UAV enabled wireless systems via DRL. In Proceedings of the IEEE 6th International Conference on Computer and Communications (ICCC). IEEE, 220–226.

[113]

Canhui Zhong, Jianping Yao, and Jie Xu. 2019. Secure UAV communication with cooperative jamming and trajectory control. IEEE Commun. Lett. 23, 2 (2019), 286–289.

[114]

Guangchi Zhang, Qingqing Wu, Miao Cui, and Rui Zhang. 2019. Securing UAV communications via joint trajectory and power control. IEEE Trans. Wirel. Commun. 18, 2 (2019), 1376–1389.

Digital Library

[115]

Yufang Gao, Yang Wu, Zhichao Cui, et al. 2020. Robust trajectory and scheduling design for turning angle-constrained UAV communications. In Proceedings of the International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 962–967.

[116]

Yufang Gao, Yang Wu, Zhichao Cui, et al. 2021. Anti-jamming trajectory and power design for cognitive UAV communications. In Proceedings of the International Wireless Communications and Mobile Computing (IWCMC). IEEE, 1370–13757.

[117]

Yifan Xu, Guochun Ren, Jin Chen, et al. 2017. Anti-jamming transmission in UAV communication networks: A Stackelberg game approach. In Proceedings of the IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 1–6.

[118]

Han-Ting Ye, Xin Kang, Jingon Joung, and Ying-Chang Liang. 2020. Optimization for full-duplex rotary-wing UAV-enabled wireless-powered IoT networks. IEEE Trans. Wirel. Commun. 9, 7 (2020), 5057–5072.

Digital Library

[119]

Yong Zeng and Rui Zhang. 2017. Energy-efficient UAV communication with trajectory optimization. IEEE Trans. Wirel. Commun. 16, 6 (2017), 3747–3760.

Digital Library

[120]

Xuan Yang, Zipeng Li, Xiaohu Ge, and Han-Chieh Chao. 2020. Energy-efficiency optimization of UAV-assisted internet of things. In Proceedings of the IEEE 6th International Conference on Computer and Communications (ICCC). IEEE, 934–940.

[121]

Ju-Hyung Lee, Ki-Hong Park, Young-Chai Ko, and Mohamed-Slim Alouini. 2020. A UAV-mounted free space optical communication: Trajectory optimization for flight time. IEEE Trans. Wirel. Commun. 19, 3 (2020), 1610–1621.

Digital Library

[122]

Cheng Zhan and Hong Lai. 2019. Energy minimization in internet-of-things system based on rotary-wing UAV. IEEE Wirel. Commun. Lett. 8, 5 (2019), 1341–1344.

[123]

Liang Zhang, Abdulkadir Celik, Shuping Dang, and Basem Shihada. 2021. Energy-efficient trajectory optimization for UAV-assisted IoT networks. IEEE Trans. Mob. Comput. DOI:

Digital Library

[124]

Yong Zeng, Jie Xu, and Rui Zhang. 2019. Energy minimization for wireless communication with rotary-wing UAV. IEEE Trans. Wirel. Commun. 18, 4 (2019), 2329–2345.

[125]

Zhen Wang, Wenjun Xu, Dingcheng Yang, and Jiaru Lin. 2019. Joint trajectory optimization and user scheduling for rotary-wing UAV-enabled wireless powered communication networks. IEEE Access 7 (2019), 181369–181380.

[126]

Seongah Jeong, Osvaldo Simeone, and Joonhyuk Kang. 2018. Mobile edge computing via a UAV-mounted cloudlet: Optimization of bit allocation and path planning. IEEE Trans. Vehic. Technol. 67, 3 (2018), 2049–2063.

[127]

Yingsheng Peng, Yong Liu, and Han Zhang. 2021. Deep reinforcement learning based path planning for UAV-assisted edge computing networks. In Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC). IEEE, (2021).

Digital Library

[128]

Sheikh Salman Hassan, Seok Won Kang, and Choong Seon Hong. 2019. Unmanned aerial vehicle waypoint guidance with energy efficient path planning in smart factory. In Proceedings of the 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 1–4.

[129]

Qin Yang and Sang-Jo Yoo. 2018. Optimal UAV path planning: Sensors data acquisition over IoT sensor networks using multi-objective bio-inspired algorithms. IEEE Access 6 (2018), 13671–13684.

[130]

Shichao Zhu, Lin Gui, Nan Cheng, et al. 2020. Joint design of access point selection and path planning for UAV-assisted cellular networks. IEEE Internet Things J. 7, 1 (2020), 220–233.

[131]

Lin Shi, Shoukun Xu, Haoyu Liu, and Zhongxu Zhan. 2020. QoS-aware UAV coverage path planning in 5G mmWave network. Comput. Netw. 175, 12 (2020), 107207.

[132]

Yao Du, Kezhi Wang, Kun Yang, and Guopeng Zhang. 2018. Energy-efficient resource allocation in UAV-based MEC system for IoT devices. In Proceedings of theIEEE Global Communications Conference (GLOBECOM). IEEE, 1–6.

Digital Library

[133]

Yuwen Qian, Feifei Wang, and Jun Li. 2019. User association and path planning for UAV-aided mobile edge computing with energy restriction. IEEE Wirel. Commun. Lett. 8, 5 (2019), 1312–1315.

[134]

Qian Liu, Long Shi, Linlin Sun, et al. 2020. Path planning for UAV-mounted mobile edge computing with deep reinforcement learning. IEEE Trans. Vehic. Technol. 69, 5 (2020), 5723–5728.

[135]

Jing Li, Yonghua Xiong, Jinhua She, and Min Wu. 2020. A path planning method for sweep coverage with multiple UAVs. IEEE Internet Things J. 7, 9 (2020), 8967–8978.

[136]

V. Gonzalez, C. A. Monje, S. Garrido, L. Moreno, and C. Balaguer. 2020. Coverage mission for UAVs using differential evolution and fast marching square methods. IEEE Aerosp. Electron. Syst. Mag. 35, 2 (2020), 18–29.

[137]

Giovanni Sanna, Simone Godio, and Giorgio Guglieri. 2021. Neural network based algorithm for multi-UAV coverage path planning. In Proceedings of the International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 1210–1217.

[138]

Xiangling Li, Wei Feng, and Yunfei Chen. 2020. Maritime coverage enhancement using UAVs coordinated with hybrid satellite-terrestrial networks. IEEE Trans. Commun. 68, 4 (2020), 2355–2369.

[139]

Mohammad Mozaffari, Walid Saad, Mehdi Bennis, and Mérouane Debbah. 2016. Unmanned aerial vehicle with underlaid device-to-device communications: Performance and tradeoffs. IEEE Trans. Wirel. Commun. 15, 6 (2016), 3949–3963.

[140]

Chi Harold Liu, Xiaoxin Ma, Xudong Gao, and Jian Tang. 2020. Distributed energy-efficient multi-UAV navigation for long-term communication coverage by deep reinforcement learning. IEEE Trans. Mob. Comput. 19, 6 (2020), 1274–1285.

[141]

Zhengrui Huang, Chongcheng Chen, and Miaoxin Pan. 2020. Multiobjective UAV path planning for emergency information collection and transmission. IEEE Internet Things J. 7, 8 (2020), 6993–7009.

[142]

Yinggao Yue, Jianqing Li, Hehong Fan, and Qin Qin. 2016. Optimization-based artificial bee colony algorithm for data collection in large-scale mobile wireless sensor networks. J. Sensors (2016), 1–12.

[143]

Jiqiang Tang, Songtao Guo, and Yuanyuan Yang. 2015. Delivery latency minimization in wireless sensor networks with mobile sink. In Proceedings of the IEEE International Conference on Communications (ICC). IEEE, 6481–6486.

[144]

Fei Yin, Zhenhong Li, and Haifeng Wang. 2013. Energy-efficient data collection in multiple mobile gateways WSN-MCN convergence system. In Proceedings of the IEEE 10th Consumer Communications Network Conference (CCNC). 271–276.

[145]

Gurudevi C. Vanarotti, Umesh M. Kulkarni, and Harish H. Kenchannavar. 2016. Ferry based data gathering in wireless sensor networks. In Proceedings of the 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT). IEEE, 165–170.

[146]

You-Chiun Wang and Kuan-Chung Chen. 2019. Efficient path planning for a mobile sink to reliably gather data from sensors with diverse sensing rates and limited buffers. IEEE Trans. Mob. Comput. 18, 7 (2019), 1527–1540.

[147]

Chatchai Punriboon, Chakchai So-In, Phet Aimtongkham, and Nutthanon Leelathakul. 2021. Fuzzy logic-based path planning for data gathering mobile sinks in WSNs. IEEE Access, 9 (2021), 96002–96020.

[148]

Robert Pěnička, Jan Faigl, and Martin Saska. 2019. Physical orienteering problem for unmanned aerial vehicle data collection planning in environments with obstacles. IEEE Robot. Autom. Lett. 4, 3 (2019), 3005–3012.

[149]

Noralifah Annuar, Neil Bergmann, Raja Jurdak, and Branislav Kusy. 2017. Mobile data collection from sensor networks with range-dependent data rates. In Proceedings of the IEEE 42nd Conference on Local Computer Networks Workshops. IEEE, 53–60.

[150]

Vrajesh Kumar Chawra and Govind. P. Gupta. 2020. Multiple UAV path-planning for data collection in cluster-based wireless sensor network. In Proceedings of the 1st International Conference on Power, Control and Computing Technologies (ICPC2T). IEEE, 194–198.

[151]

Marija Popović, Teresa Vidal-Calleja, Gregory Hitz, and Inkyu Sa. 2017. Multiresolution mapping and informative path planning for UAV-based terrain monitoring. 2020. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, (2017).

[152]

Mbazingwa E. Mkiramweni, Chungang Yang, Jiandong Li, and Zhu Han. 2018. Game-theoretic approaches for wireless communications with unmanned aerial vehicles. IEEE Wirel. Commun. 25, 6 (2018), 104–112.

Digital Library

[153]

Zan Li, Xiaomin Liao, Jia Shi, Li Li, and Pei Xiao. 2021. MD-GAN based UAV trajectory and power optimization for cognitive covert communications. IEEE Internet Things J. 9, 12 (2021), 10187–10199.

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Index Terms

Path Planning for UAV Communication Networks: Related Technologies, Solutions, and Opportunities
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
2. Computing methodologies

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 9

September 2023

835 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3567474

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 16 January 2023

Online AM: 05 September 2022

Accepted: 21 August 2022

Revision received: 08 August 2022

Received: 24 November 2021

Published in CSUR Volume 55, Issue 9

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Cited By

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https://doi.org/10.4018/979-8-3693-2093-8.ch023
Li JGeng QMa FWang C(2024)A 3D UAV trajectory planning method based on Improved Convex Corner A-star algorithm2024 43rd Chinese Control Conference (CCC)10.23919/CCC63176.2024.10662603(3791-3796)Online publication date: 28-Jul-2024
https://doi.org/10.23919/CCC63176.2024.10662603
Yan MXiong RWang YLi C(2024)Edge Computing Task Offloading Optimization for a UAV-Assisted Internet of Vehicles via Deep Reinforcement LearningIEEE Transactions on Vehicular Technology10.1109/TVT.2023.333136373:4(5647-5658)Online publication date: Apr-2024
https://doi.org/10.1109/TVT.2023.3331363
Liu HChen RZhang JJiang ZLu G(2024)Optimizing Data Transmission in Large-Scale Sensor Networks Using UAVs: A Segmentation-Clustering and Sequential ApproachIEEE Sensors Journal10.1109/JSEN.2024.342456024:16(26770-26787)Online publication date: 15-Aug-2024
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Banafaa MPepeoğlu ÖShayea IAlhammadi AShamsan ZRazaz MAlsagabi MAl-Sowayan S(2024)A Comprehensive Survey on 5G-and-Beyond Networks With UAVs: Applications, Emerging Technologies, Regulatory Aspects, Research Trends and ChallengesIEEE Access10.1109/ACCESS.2023.334920812(7786-7826)Online publication date: 2024
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Meng XGao HWang JWang XYu HZhang J(2024)Dynamic path planning of autonomous bulldozers using activity-value-optimised bio-inspired neural networks and adaptive cell decompositionApplied Soft Computing10.1016/j.asoc.2024.111944164(111944)Online publication date: Oct-2024
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