Intelligent Unmanned Aerial Vehicles for Low-altitude Economy: A Review of the Technology Framework and Future Prospects

QIAN Zhihong; WANG Yijun

doi:10.11999/JEIT251246

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2026 >

QIAN Zhihong, WANG Yijun. Intelligent Unmanned Aerial Vehicles for Low-altitude Economy: A Review of the Technology Framework and Future Prospects[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251246

Citation:

QIAN Zhihong, WANG Yijun. Intelligent Unmanned Aerial Vehicles for Low-altitude Economy: A Review of the Technology Framework and Future Prospects[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251246

Citation:

PDF( 6280 KB)

Intelligent Unmanned Aerial Vehicles for Low-altitude Economy: A Review of the Technology Framework and Future Prospects

doi: 10.11999/JEIT251246 cstr: 32379.14.JEIT251246

QIAN Zhihong¹,
WANG Yijun^{2
,
,}

1.
School of Communication Engineering, Jilin University, Changchun 130012, China
2.
School of Electronic & Information Engineering, Changchun University of Science and Technology, Changchun 130022, China

Funds: The National Natural Science Foundation of China (61771219), Jilin Provincial Natural Science Foundation (20250102227JC).

Received Date: 2025-11-25
Accepted Date: 2025-12-22
Rev Recd Date: 2025-12-20

Available Online: 2026-01-02

Abstract

Abstract

Significance The deep integration of new quality productive forces with the digital economy accelerates the development of the low-altitude economy and positions it as an emerging driver of global economic growth. Operating in airspace typically below 3 000 m, this industrial system supports diverse applications, including Unmanned Aerial Vehicle (UAV) logistics, Urban Air Mobility (UAM), industrial inspection, and public safety. Intelligent UAVs, characterized by cost efficiency, scalability, and autonomous capability, function as the core technical enabler of this ecosystem. Their deployment promotes a transition in aviation from centralized and isolated operation modes toward distributed, intelligent, and service-oriented aerial utilization. From a strategic perspective, intelligent UAVs contribute to industrial upgrading, urban infrastructure improvement, airspace security assurance, and regional economic development. Therefore, a systematic review and structured construction of an intelligent UAV technology framework is necessary to support future research, clarify key challenges, and promote sustained development of the low-altitude economy. Progress A holistic technology framework for intelligent UAVs is constructed, organized hierarchically from foundational technologies to application-oriented systems. The framework integrates four interrelated domains. Intelligent perception and navigation emphasize stable operation in complex environments through tightly coupled multi-sensor fusion and advanced state estimation methods, such as visual-inertial odometry, supported by multi-source adaptive positioning in Global Navigation Satellite System (GNSS)-denied scenarios. Wireless communication networks focus on reliable Beyond-Visual-Line-Of-Sight (BVLOS) connectivity by combining cellular network access, self-organizing flying ad hoc networks (FANETs) with intelligent topology control, and UAV-assisted edge computing for efficient resource scheduling. Autonomous decision-making and cooperative control evolve from classical rule-based approaches toward learning-based paradigms, where multi-agent reinforcement learning enables coordinated swarm behavior and adaptive task execution. Low-altitude security and airspace management provide essential system support through integrated detection and countermeasure technologies, supplemented by UAV cloud platforms and Unmanned aircraft system Traffic Management (UTM) for coordinated airspace operation. Conclusions The review indicates that UAVs are transitioning from isolated platforms to interconnected intelligent nodes embedded within the low-altitude economy system. Although substantial progress has been achieved across multiple technological domains, several critical challenges remain. Major technical constraints include maintaining communication reliability in complex low-altitude channels, addressing perception degradation in cluttered or deceptive environments, achieving robust autonomous cooperation under uncertainty, and overcoming the inherent limitations of existing energy and power technologies. These technical issues coexist with non-technical barriers, such as the establishment of adaptive regulatory and airspace governance frameworks, the formation of scalable and sustainable business models, and the enhancement of public acceptance. The analysis suggests that addressing these challenges requires deep integration of enabling technologies. A closed-loop evolution paradigm of “challenge-driven → technology fusion → system construction → feedback iteration” is proposed to describe the intrinsic iterative logic of technological development and to provide methodological guidance for future research and engineering practice. Prospects Future intelligent UAV development is expected to concentrate on several strongly coupled directions. Intelligent holistic communication will advance through deep integration of air-ground-space networks and Integrated Sensing And Communication (ISAC), forming a proactive data environment that supports predictive resource management and resilient connectivity. Cognitive swarm intelligence will promote the transformation of UAV clusters into cooperative cognitive systems by combining large language models for task comprehension with multi-agent reinforcement learning for decentralized decision-making, enabling emergent collective intelligence. High-assurance autonomous security will rely on formal verification of artificial intelligence models, explainable decision mechanisms, and extensive application of digital twins for virtual validation and certification, thereby strengthening operational trust. In parallel, green and sustainable technologies will influence the full lifecycle of UAV systems, encouraging advances in high-energy-density power solutions, including solid-state batteries and hydrogen fuel cells, the use of environmentally friendly materials, and artificial intelligence-based optimization of energy consumption and acoustic performance, which together support the long-term sustainability of the low-altitude economy.
- Low-altitude economy,
- Intelligent Unmanned Aerial Vehicle(UAV),
- Technology system,
- Closed-loop evolution paradigm

FullText(HTML)

References(108)

References

[1]	WANG Yixian, SUN Geng, SUN Zemin, et al. Toward realization of low-altitude economy networks: Core architecture, integrated technologies, and future directions[J]. IEEE Transactions on Cognitive Communications and Networking, 2025, 11(5): 2788–2820. doi: 10.1109/TCCN.2025.3601015.
[2]	国务院, 中央军委. 低空空域管理改革方案[Z]. 北京: 2010. State Council and Central Military Commission of the People’s Republic of China. Low altitude airspace management reform plan[Z]. Beijing, China, 2010.
[3]	中国信息通信研究院. 低空经济白皮书(2024年)[R]. 北京: 中国信息通信研究院, 2024. China Academy of Information and Communications Technology (CAICT). White paper on low-altitude economy (2024)[R]. Beijing, China: China Academy of Information and Communications Technology, 2024.
[4]	World Economic Forum. Advanced air mobility: Shaping the future of aviation[R]. Davos: World Economic Forum, 2024.
[5]	National Aeronautics and Space Administration. UTM: Air traffic management for low-altitude drones[R]. Washington, DC: National Aeronautics and Space Administration, 2018.
[6]	NASA. Unmanned aircraft system traffic management concepts of operations V2.0[R]. Washington DC, 2020.
[7]	Japan Aerospace Exploration Agency. Roadmap for the application and technology development of UAVs[R]. Toyota, Japan, 2017.
[8]	王俊潼, 包丹文, 周佳怡, 等. 低空空域规划研究现状与展望[J]. 航空学报, 2025, 46(11): 530879. doi: 10.7527/S1000-6893.2024.30879. WANG Juntong, BAO Danwen, ZHOU Jiayi, et al. Low-altitude airspace planning: A review and prospect[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 530879. doi: 10.7527/S1000-6893.2024.30879.
[9]	张平, 陈岩, 吴超楠. 6G: 新一代移动通信技术发展态势及展望[J]. 中国工程科学, 2023, 25(6): 1–8. doi: 10.15302/J-SSCAE-2023.06.001. ZHANG Ping, CHEN Yan, and WU Chaonan. Six-generation mobile communication: Development trend and outlook[J]. Strategic Study of CAE, 2023, 25(6): 1–8. doi: 10.15302/J-SSCAE-2023.06.001.
[10]	WANG Linan and WEN Guanghui. Attitude estimation for rigid aircraft: A distributed finite-time complementary filter with multiple inertial measurement units[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 3004010. doi: 10.1109/TIM.2025.3612644.
[11]	谷美颖, 李航, 张家伟, 等. 基于视觉的无人机定位与导航方法研究综述[J]. 电子学报, 2025, 53(3): 651–685. doi: 10.12263/DZXB.20240699. GU Meiying, LI Hang, ZHANG Jiawei, et al. A review of vision-based UAV localization and navigation methods[J]. Acta Electronica Sinica, 2025, 53(3): 651–685. doi: 10.12263/DZXB.20240699.
[12]	LI Yiyuan, CHEN Weiyi, FU Bing, et al. A distributed cooperative dynamic target search method for multi-UAV systems in complex adversarial environments[J]. IEEE Internet of Things Journal, 2025, 12(18): 38155–38171. doi: 10.1109/JIOT.2025.3585158.
[13]	CHENG Yuanxun, HU Qingsong, GAO Wenjie, et al. Environment-aware IoT UAV channel prediction: A multiparameter prediction case using multimodal sensing data[J]. IEEE Internet of Things Journal, 2025, 24(12): 53410–53426. doi: 10.1109/JIOT.2025.3616151.
[14]	CHEN Xu, MA Chunguang, ZHAO Chaofan, et al. UAV classification based on deep learning fusion of multidimensional UAV micro-Doppler image features[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 3503205. doi: 10.1109/LGRS.2024.3371171.
[15]	MICLEA V C and NEDEVSCHI S. Dynamic semantically guided monocular depth estimation for UAV environment perception[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5605111. doi: 10.1109/TGRS.2023.3345475.
[16]	CHO E, KIM H, KIM P, et al. Obstacle avoidance of a UAV using fast monocular depth estimation for a wide stereo camera[J]. IEEE Transactions on Industrial Electronics, 2025, 72(2): 1763–1773. doi: 10.1109/TIE.2024.3429611.
[17]	QIN Tong, LI Peiliang, and SHEN Shaojie. VINS-mono: A robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004–1020. doi: 10.1109/TRO.2018.2853729.
[18]	FROSI M and MATTEUCCI M. ART-SLAM: Accurate real-time 6DoF LiDAR SLAM[J]. IEEE Robotics and Automation Letters, 2022, 7(2): 2692–2699. doi: 10.1109/LRA.2022.3144795.
[19]	CHEN Yu, XU Bo, WANG Bin, et al. GNSS reconstrainted visual–inertial odometry system using factor graphs[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 8000305. doi: 10.1109/LGRS.2023.3236803.
[20]	SONG Boyi, YUAN Xianfeng, YING Zhongmou, et al. DGM-VINS: Visual–inertial SLAM for complex dynamic environments with joint geometry feature extraction and multiple object tracking[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 8503711. doi: 10.1109/TIM.2023.3280533.
[21]	DENG Min, HU Jiwei, WEN Junxiang, et al. Object detection based visual SLAM optimization method for dynamic scene[J]. IEEE Sensors Journal, 2025, 25(9): 16480–16488. doi: 10.1109/JSEN.2025.3552797.
[22]	CAO Zhengyang and CHEN Gang. Enhanced deep reinforcement learning for integrated navigation in multi-UAV systems[J]. Chinese Journal of Aeronautics, 2025, 38(8): 103497. doi: 10.1016/j.cja.2025.103497.
[23]	JIANG Yingying, ZHU Ni, and RENAUDIN V. A voting-based robust estimator aided by INS redundancy for tightly coupled GNSS/INS integration in urban environment[J]. IEEE Transactions on Vehicular Technology, 2025, 74(9): 13430–13445. doi: 10.1109/TVT.2025.3560363.
[24]	HU Gaoge, WANG Wei, ZHONG Yongmin, et al. A new direct filtering approach to INS/GNSS integration[J]. Aerospace Science and Technology, 2018, 77: 755–764. doi: 10.1016/j.ast.2018.03.040.
[25]	CANH T N, NGUYEN V T, HOANGVAN X, et al. S3M: Semantic segmentation sparse mapping for UAVs with RGB-D camera[C].2024 IEEE/SICE International Symposium on System Integration (SII), 2024, Ha Long, Vietnam, 2024: 899–905. doi: 10.1109/SII58957.2024.10417379.
[26]	ZHAI Rui and YUAN Yunbin. A method of vision aided GNSS positioning using semantic information in complex urban environment[J]. Remote Sensing, 2022, 14(4): 869. doi: 10.3390/rs14040869.
[27]	KANG Xu, WANG Dejiang, SHAO Yu, et al. An efficient hybrid multi-station TDOA and single-station AOA localization method[J]. IEEE Transactions on Wireless Communications, 2023, 22(8): 5657–5670. doi: 10.1109/TWC.2023.3235753.
[28]	LIU Qi, GAO Chengfa, SHANG Rui, et al. Hybrid GNSS+5G position and rotation estimation algorithm based on TOA and unit vector of arrival in urban environment[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 9512908. doi: 10.1109/TIM.2024.3427761.
[29]	CHEN Kai, LIANG Wenchao, and ZENG Chengzhi. Large-scale geomagnetic navigation for high-speed aircraft based on the gradient extraction neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 5921111. doi: 10.1109/TGRS.2025.3604619.
[30]	MENG Chen, HU Qinglei, GE S S, et al. Trusted multisource fusion navigation for UAV under GNSS interference and spoofing attacks[J]. IEEE/ASME Transactions on Mechatronics, 2025: 1–11. doi: 10.1109/TMECH.2025.3570315.
[31]	LI Zhen, TAO Jun, LEI Zhuo, et al. Factor graph optimization-based RTK/INS integration with raw observations for robust positioning in urban canyons[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 9523511. doi: 10.1109/TIM.2025.3577823.
[32]	胡杨林, 张天魁, 李博, 等. 无人机使能的通信感知一体化组网与技术研究综述[J]. 电子与信息学报, 2025, 47(4): 859–875. doi: 10.11999/JEIT241116. HU Yanglin, ZHANG Tiankui, LI Bo, et al. A survey on UAV-enabled integrated sensing and communication networking and technologies[J]. Journal of Electronics & Information Technology, 2025, 47(4): 859–875. doi: 10.11999/JEIT241116.
[33]	LI Sixian, DUO Bin, YUAN Xiaojun, et al. Reconfigurable intelligent surface assisted UAV communication: Joint trajectory design and passive beamforming[J]. IEEE Wireless Communications Letters, 2020, 9(5): 716–720. doi: 10.1109/LWC.2020.2966705.
[34]	HUA Meng, YANG Luxi, WU Qingqing, et al. UAV-assisted intelligent reflecting surface symbiotic radio system[J]. IEEE Transactions on Wireless Communications, 2021, 20(9): 5769–5785. doi: 10.1109/TWC.2021.3070014.
[35]	HUA Meng, YANG Luxi, WU Qingqing, et al. 3D UAV trajectory and communication design for simultaneous uplink and downlink transmission[J]. IEEE Transactions on Communications, 2020, 68(9): 5908–5923. doi: 10.1109/TCOMM.2020.3003662.
[36]	MU Xidong, LIU Yuanwei, GUO Li, et al. Intelligent reflecting surface enhanced multi-UAV NOMA networks[J] IEEE Journal on Selected Areas in Communications, 2021, 39(10): 3051–3066. doi: 10.1109/JSAC.2021.3088679.
[37]	FANG Sisai, CHEN Gaojie, and LI Yonghui. Joint optimization for secure intelligent reflecting surface assisted UAV networks[J]. IEEE Wireless communications Letters, 2021, 10(2): 276–280. doi: 10.1109/LWC.2020.3027969.
[38]	CHEN Songchao, LIU Fang, and LIU Yuanan. Sum rate maximization for intelligent reflecting surface-assisted UAV-enabled NOMA network[J]. Electronics, 2023, 12(17): 3616. doi: 10.3390/electronics12173616.
[39]	ZHAO Bai, GUO Yan, XU Ba, et al. Combined UAV positioning with robust beamforming and IRS-enhanced NOMA transmission in cognitive UAV networks[J]. AEU-International Journal of Electronics and Communications, 2023, 168: 154727. doi: 10.1016/j.aeue.2023.154727.
[40]	SHAKHATREH H, SAWALMEH A, ALENEZI A H, et al. Mobile-IRS assisted next generation UAV communication networks[J]. Computer Communications, 2024, 215: 51–61. doi: 10.1016/j.comcom.2023.12.025.
[41]	ZHAO Yikun, ZHOU Fanqin, LIU Huaide, et al. PPO-based deployment and phase control for movable intelligent reflecting surface[J]. Journal of Cloud Computing, 2023, 12(1): 168. doi: 10.1186/s13677-023-00528-1.
[42]	ALAM M M, ARAFAT M Y, MOH S, et al. Topology control algorithms in multi-unmanned aerial vehicle networks: An extensive survey[J]. Journal of Network and Computer Applications, 2022, 207: 103495. doi: 10.1016/j.jnca.2022.103495.
[43]	刘亚群, 谢钧, 邢长友, 等. 飞行自组网拓扑控制研究综述[J]. 通信学报, 2023, 44(8): 195–214. doi: 10.11959/j.issn.1000-436x.2023155. LIU Yaqun, XIE Jun, XING Changyou, et al. Comprehensive survey on topology control for flying ad-hoc network[J]. Journal on Communications, 2023, 44(8): 195–214. doi: 10.11959/j.issn.1000-436x.2023155.
[44]	GUO Qiang, YAN Jichen, and XU Wei. Localized fault tolerant algorithm based on node movement freedom degree in flying ad hoc networks[J]. Symmetry, 2019, 11(1): 106. doi: 10.3390/sym11010106.
[45]	LIU Yaqun, XIE Jun, XING Changyou, et al. Topology construction and topology adjustment in flying Ad hoc networks for relay transmission[J]. Computer Networks, 2023, 228: 109753. doi: 10.1016/j.comnet.2023.109753.
[46]	BASU P and REDI J. Movement control algorithms for realization of fault-tolerant ad hoc robot networks[J]. IEEE Network, 2004, 18(4): 36–44. doi: 10.1109/MNET.2004.1316760.
[47]	LIU Chao and ZHANG Zhongshan. Towards a robust FANET: Distributed node importance estimation-based connectivity maintenance for UAV swarms[J]. Ad Hoc Networks, 2022, 125: 102734. doi: 10.1016/j.adhoc.2021.102734.
[48]	GAYDAMAKA A, SAMUYLOV A, MOLTCHANOV D, et al. Dynamic topology organization and maintenance algorithms for autonomous UAV swarms[J]. IEEE Transactions on Mobile Computing, 2024, 23(5): 4423–4439. doi: 10.1109/TMC.2023.3293034.
[49]	WANG Huibin, CHEN Ming, and WEI Xianglin. A k-hop constrained reachability based proactive connectivity maintaining mechanism of UAV swarm networks[J]. Journal of Internet Technology, 2023, 24(6): 1329–1341. doi: 10.53106/160792642023112406015.
[50]	TOSUN M, CABUK U C, HAYTAOGLU E, et al. DPkCR: Distributed proactive k-connectivity recovery algorithm for UAV-based MANETs[J]. IEEE Transactions on Reliability, 2024, 73(4): 1918–1932. doi: 10.1109/TR.2024.3370743.
[51]	SUN Wenbin, ZHAO Lei, YANG Xin, et al. Joint topology reconstruction and resource allocation for UAV-IoT networks[J]. IEEE Internet of Things Journal, 2024, 11(22): 36452–36464. doi: 10.1109/JIOT.2024.3406045.
[52]	ZHANG Le, DU Ye, XU Jinqi, et al. UAV-enabled IoT: Cascading failure model and topology-control-based recovery scheme[J]. IEEE Internet of Things Journal, 2024, 11(12): 22562–22577. doi: 10.1109/jiot.2024.3381735.
[53]	FU Luwei, ZHAO Zhiwei, MIN Geyong, et al. Towards accurate and low-cost path reconstruction in mobile UAV networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(11): 17279–17290. doi: 10.1109/TITS.2024.3408811.
[54]	王侃, 曹铁林, 李旭杰, 等. 无人机辅助边缘计算网络轨迹规划与资源分配研究综述[J]. 电子与信息学报, 2025, 47(5): 1266–1281. doi: 10.11999/JEIT241071. WANG Kan, CAO Tielin, LI Xujie, et al. A survey on trajectory planning and resource allocation in unmanned aerial vehicle-assisted edge computing networks[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1266–1281. doi: 10.11999/JEIT241071.
[55]	DENG Yiqin, CHEN Zhigang, CHEN Xianhao, et al. Task offloading in multi-hop relay-aided multi-access edge computing[J]. IEEE Transactions on Vehicular Technology, 2023, 72(1): 1372–1376. doi: 10.1109/TVT.2022.3204398.
[56]	KUANG Zhufang, PAN Yihui, YANG Fan, et al. Joint task offloading scheduling and resource allocation in air–ground cooperation UAV-enabled mobile edge computing[J]. IEEE Transactions on Vehicular Technology, 2024, 73(4): 5796–5807. doi: 10.1109/TVT.2023.3334143.
[57]	WANG Xiong, YE Jiancheng, and LIU J C S. Online learning aided decentralized multi-user task offloading for mobile edge computing[J]. IEEE Transactions on Mobile Computing, 2024, 23(4): 3328–3342. doi: 10.1109/TMC.2023.3275851.
[58]	王义君, 李嘉欣, 闫志颖, 等. 基于深度强化学习的移动边缘计算安全传输策略研究[J]. 通信学报, 2025, 46(4): 272–281. doi: 10.11959/j.issn.1000-436x.2025060. WANG Yijun, LI Jiaxin, YAN Zhiying, et al. Research on secure transport strategy of mobile edge computing based on deep reinforcement learning[J]. Journal on Communications, 2025, 46(4): 272–281. doi: 10.11959/j.issn.1000-436x.2025060.
[59]	ZHOU Lei, CHEN Ying, LI Kaixin, et al. Stackelberg-game-based computation offloading in urban IoT systems with AAV-assisted multiaccess edge computing[J]. IEEE Internet of Things Journal, 2025, 12(7): 8178–8191. doi: 10.1109/JIOT.2024.3505143.
[60]	ZHANG Jianshan, LUO Haibo, CHEN Xing, et al. Minimizing response delay in UAV-assisted mobile edge computing by joint UAV deployment and computation offloading[J]. IEEE Transactions on Cloud Computing, 2024, 12(4): 1372–1386. doi: 10.1109/TCC.2024.3478172.
[61]	WANG Boxiong, KANG Hui, LI Jiahui, et al. AAV-assisted joint mobile edge computing and data collection via matching-enabled deep reinforcement learning[J]. IEEE Internet of Things Journal, 2025, 12(12): 19782–19800. doi: 10.1109/JIOT.2025.3542025.
[62]	CHEN Haosheng, CUI Haixia, WANG Jiahuan, et al. Computation offloading optimization for UAV-based cloud-edge collaborative task scheduling strategy[J]. IEEE Transactions on Cognitive Communications and Networking, 2025, 11(6): 4240–4253. doi: 10.1109/TCCN.2025.3544822.
[63]	CHEN Zhuoyue, YANG Yaozong, XU Jiajie, et al. Task offloading and resource pricing based on game theory in UAV-assisted edge computing[J]. IEEE Transactions on Services Computing, 2025, 18(1): 440–452. doi: 10.1109/TSC.2024.3512936.
[64]	JI Jiequ, ZHU Kun, NIYATO D, et al. Joint cache placement, flight trajectory, and transmission power optimization for multi-UAV assisted wireless networks[J]. IEEE Transactions on Wireless Communications, 2020, 19(8): 5389–5403. doi: 10.1109/TWC.2020.2992926.
[65]	ZHANG Mingze, EI-HAJJAR M, and NG S X. Intelligent caching in UAV-aided networks[J]. IEEE Transactions on Vehicular Technology, 2022, 71(1): 739–752. doi: 10.1109/TVT.2021.3125396.
[66]	ZHOU Fasheng, WANG Ning, LUO Gaoyong, et al. Edge caching in multi-UAV-enabled radio access networks: 3D modeling and spectral efficiency optimization[J]. IEEE Transactions on Signal and Information Processing over Networks, 2020, 6: 329–341. doi: 10.1109/TSIPN.2020.2986360.
[67]	WEI Qing, CHEN Yingyang, JIA Ziye, et al. Energy-efficient caching and user selection for resource-limited SAGINs in emergency communications[J]. IEEE Transactions on Communications, 2025, 73(6): 4121–4136. doi: 10.1109/TCOMM.2024.3511958.
[68]	LI Xuanheng, LIU Jiahong, CHEN Xianhao, et al. Caching on the sky: A multiagent federated reinforcement learning approach for UAV-assisted edge caching[J]. IEEE Internet of Things Journal, 2024, 11(17): 28213–28226. doi: 10.1109/JIOT.2024.3401219.
[69]	LIU Yinan, YANG Chao, CHEN Xin, et al. Joint hybrid caching and replacement scheme for UAV-assisted vehicular edge computing networks[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9(1): 866–878. doi: 10.1109/TIV.2023.3323217.
[70]	ZHOU Ruiting, HUANG Yifeng, WANG Yufeng, et al. User preference oriented service caching and task offloading for UAV-assisted MEC networks[J]. IEEE Transactions on Services Computing, 2025, 18(2): 1097–1109. doi: 10.1109/TSC.2025.3536319.
[71]	YANG Die, ZHAN Cheng, YANG Yang, et al. Integrating UAVs and D2D communication for MEC network: A collaborative approach to caching and computation[J]. IEEE Transactions on Vehicular Technology, 2025, 74(6): 10041–10046. doi: 10.1109/TVT.2025.3540915.
[72]	MAALE G T, KUADEY N A E, ARAFAT Y, et al. Multi-task learning for UAV trajectory and caching with federated cloud-assisted knowledge distillation[J]. IEEE Transactions on Network and Service Management, 2025, 22(3): 2516–2533. doi: 10.1109/TNSM.2025.3547743.
[73]	SHEN K, SHIVGAN R, MEDINA J, et al. Multidepot drone path planning with collision avoidance[J]. IEEE Internet of Things Journal, 2022, 9(17): 16297–16307. doi: 10.1109/JIOT.2022.3151791.
[74]	SAEED R A, OMRI M, ABDEL-KHALEK S, et al. Optimal path planning for drones based on swarm intelligence algorithm[J]. Neural Computing and Applications, 2022, 34(12): 10133–10155. doi: 10.1007/s00521-022-06998-9.
[75]	JAYAWEERA H M P C and HANOUN S. Path planning of unmanned aerial vehicles (UAVs) in windy environments[J]. Drones, 2022, 6(5): 101. doi: 10.3390/drones6050101.
[76]	HUANG Xiongwei, LIU Yongping, HUANG Lizhen, et al. BIM-supported drone path planning for building exterior surface inspection[J]. Computers in Industry, 2023, 153: 104019. doi: 10.1016/j.compind.2023.104019.
[77]	YANMAZ E. Joint or decoupled optimization: Multi-UAV path planning for search and rescue[J]. Ad Hoc Networks, 2023, 138: 103018. doi: 10.1016/j.adhoc.2022.103018.
[78]	YANG Tongyao, YANG Fengbao, and LI Dingzhu. A new autonomous method of drone path planning based on multiple strategies for avoiding obstacles with high speed and high density[J]. Drones, 2024, 8(5): 205. doi: 10.3390/drones8050205.
[79]	GÜVEN İ and YANMAZ E. Multi-objective path planning for multi-UAV connectivity and area coverage[J]. Ad Hoc Networks, 2024, 160: 103520. doi: 10.1016/j.adhoc.2024.103520.
[80]	KIM M J, KANG T Y, and RYOO C K. Real-time path planning for unmanned aerial vehicles based on compensated Voronoi diagram[J]. International Journal of Aeronautical and Space Sciences, 2025, 26(1): 235–244. doi: 10.1007/s42405-024-00771-z.
[81]	WU Yu, GOU Jinzhan, HU Xinting, et al. A new consensus theory-based method for formation control and obstacle avoidance of UAVs[J]. Aerospace Science and Technology, 2020, 107: 106332. doi: 10.1016/j.ast.2020.106332.
[82]	XI Meng, WEN Jiabao, HE Jingyi, et al. An expert experience-enhanced security control approach for AUVs of the underwater transportation cyber-physical systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2025, 26(9): 14086–14098. doi: 10.1109/TITS.2024.3524730.
[83]	YAN Ziwei, HAN Liang, LI Xiaoduo, et al. Event-Triggered formation control for time-delayed discrete-Time multi-Agent system applied to multi-UAV formation flying[J]. Journal of the Franklin Institute, 2023, 360(5): 3677–3699. doi: 10.1016/j.jfranklin.2023.01.036.
[84]	CHEN Hao, WANG Xiangke, SHEN Lincheng, et al. Formation flight of fixed-wing UAV swarms: A group-based hierarchical approach[J]. Chinese Journal of Aeronautics, 2021, 34(2): 504–515. doi: 10.1016/j.cja.2020.03.006.
[85]	TAO Canhui, ZHANG Ru, SONG Zhiping, et al. Multi-UAV formation control in complex conditions based on improved consistency algorithm[J]. Drones, 2023, 7(3): 185. doi: 10.3390/drones7030185.
[86]	ZHOU Jinlun, ZHANG Honghai, HUA Mingzhuang, et al. P-DRL: A framework for multi-UAVs dynamic formation control under operational uncertainty and unknown environment[J]. Drones, 2024, 8(9): 475. doi: 10.3390/drones8090475.
[87]	MANDAL P, ROY L P, and DAS S K. Topology control of drones using bio-inspired intelligent firefly-grasshopper algorithm for searching intruder unmanned aerial vehicle[J]. IETE Journal of Research, 2024, 70(3): 2269–2285. doi: 10.1080/03772063.2023.2191999.
[88]	ASCI M, DAGDEVIREN Z A, AKRAM V K, et al. Enhancing drone network resilience: Investigating strategies for k-connectivity restoration[J]. Computer Standards & Interfaces, 2025, 92: 103941. doi: 10.1016/j.csi.2024.103941.
[89]	WANG Yuanzhe, YUE Yufeng, SHAN Mao, et al. Formation reconstruction and trajectory replanning for multi-UAV patrol[J]. IEEE/ASME Transactions on Mechatronics, 2021, 26(2): 719–729. doi: 10.1109/TMECH.2021.3056099.
[90]	GAO Chenyang, MA Jianfeng, LI Teng, et al. Hybrid swarm intelligent algorithm for multi-UAV formation reconfiguration[J]. Complex & Intelligent Systems, 2023, 9(2): 1929–1962. doi: 10.1007/s40747-022-00891-7.
[91]	HU Ye, CHEN Mingzhe, SAAD W, et al. Distributed multi-agent meta learning for trajectory design in wireless drone networks[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(10): 3177–3192. doi: 10.1109/JSAC.2021.3088689.
[92]	MORILLA-CABELLO D, BARTOLOMEI L, TEIXEIRA L, et al. Sweep-your-map: Efficient coverage planning for aerial teams in large-scale environments[J]. IEEE Robotics and Automation Letters, 2022, 7(4): 10810–10817. doi: 10.1109/LRA.2022.3194686.
[93]	LIU Xinbin, WANG Ye, GAO Hui, et al. A coverage-aware task allocation method for UAV-assisted mobile crowd sensing[J]. IEEE Transactions on Vehicular Technology, 2024, 73(7): 10642–10654. doi: 10.1109/TVT.2024.3374719.
[94]	FAN Kexin, FENG Bowen, ZHANG Xilin, et al. Demand-driven task scheduling and resource allocation in space-air-ground integrated network: A deep reinforcement learning approach[J]. IEEE Transactions on Wireless Communications, 2024, 23(10): 13053–13067. doi: 10.1109/TWC.2024.3398199.
[95]	ZHANG Jing, REN Jia, CUI Yani, et al. Multi-USV task planning method based on improved deep reinforcement learning[J]. IEEE Internet of Things Journal, 2024, 11(10): 18549–18567. doi: 10.1109/JIOT.2024.3363044.
[96]	LI Yibing, ZHANG Zitang, HE Zongyu, et al. A heuristic task allocation method based on overlapping coalition formation game for heterogeneous UAVs[J]. IEEE Internet of Things Journal, 2024, 11(17): 28945–28959. doi: 10.1109/JIOT.2024.3406336.
[97]	ZHAI Shaobo, LI Guangwen, WU Guo, et al. Cooperative task allocation for multi heterogeneous aerial vehicles using particle swarm optimization algorithm and entropy weight method[J]. Applied Soft Computing, 2023, 148: 110918. doi: 10.1016/j.asoc.2023.110918.
[98]	TAN Yifang, ZHOU Chao, and QIAN Feng. Cooperative task allocation method for multi-unmanned aerial vehicles based on the modified genetic algorithm[J]. IET Intelligent Transport Systems, 2024, 18(6): 1164–1173. doi: 10.1049/itr2.12495.
[99]	LI Xueqing, LU Xinpeng, CHEN Wenhao, et al. Research on UAVs reconnaissance task allocation method based on communication preservation[J]. IEEE Transactions on Consumer Electronics, 2024, 70(1): 684–695. doi: 10.1109/TCE.2024.3368062.
[100]	LI Mincan, WANG Zidong, LI Kenli, et al. Task allocation on layered multiagent systems: When evolutionary many-objective optimization meets deep Q-learning[J]. IEEE Transactions on Evolutionary Computation, 2021, 25(5): 842–855. doi: 10.1109/TEVC.2021.3049131.
[101]	YI Bo, LV Jianhui, CHEN Jiahao, et al. Digital twin constructed spatial structure for flexible and efficient task allocation of drones in mobile networks[J]. IEEE Journal on Selected Areas in Communications, 2023, 41(11): 3430–3443. doi: 10.1109/JSAC.2023.3313193.
[102]	WANG Shengli, LIU Youjiang, QIU Yongtao, et al. Consensus-based decentralized task allocation for multi-agent systems and simultaneous multi-agent tasks[J]. IEEE Robotics and Automation Letters, 2022, 7(4): 12593–12600. doi: 10.1109/LRA.2022.3220155.
[103]	YE Fang, CHEN Jie, SUN Qian, et al. Decentralized task allocation for heterogeneous multi-UAV system with task coupling constraints[J]. The Journal of Supercomputing, 2021, 77(1): 111–132. doi: 10.1007/s11227-020-03264-4.
[104]	贾永楠. 低空空域无人系统交通管理方案初探[J]. 航空学报, 2025, 46(11): 531399. doi: 10.7527/S1000-6893.2025.31399. JIA Yongnan. A scheme for unmanned aerial system traffic management in low-altitude airspace[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 531399. doi: 10.7527/S1000-6893.2025.31399.
[105]	YAZICI A and BAYKAL B. Detection and localization of drones in MIMO CW radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(1): 226–238. doi: 10.1109/TAES.2023.3321586.
[106]	SAYED A N, RAMAHI O M, and SHAKER G. Machine learning for UAV classification employing mechanical control information[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(1): 68–81. doi: 10.1109/TAES.2023.3272303.
[107]	SHOUFAN A and DAMIANI E. Contingency clarification protocols for reliable counter-drone operation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(6): 8944–8955. doi: 10.1109/TAES.2023.3313573.
[108]	钱志鸿, 田春生, 郭银景, 等. 智能网联交通系统的关键技术与发展[J]. 电子与信息学报, 2020, 42(1): 2–19. doi: 10.11999/JEIT190787. QIAN Zhihong, TIAN Chunsheng, GUO Yinjing, et al. The key technology and development of intelligent and connected transportation system[J]. Journal of Electronics & Information Technology, 2020, 42(1): 2–19. doi: 10.11999/JEIT190787.