Intelligent flying cars: Driving future of urban air mobility
Article Text (Baidu Translation)
-
摘要: 系统梳理了智能飞行汽车(IFC)的关键技术进展，主要从车体设计与动力系统、自主导航与控制技术、车路云协同技术3个层面展开综述；总结了涵道风扇、折叠翼、分体式等多样化构型及混合动力、燃料电池等能源架构的发展现状；重点回顾了多传感器融合、深度学习路径规划与鲁棒控制等自主导航与控制方法；探讨了低空通信网络、协同感知和智能调度在车路云一体化架构中的应用；分析了IFC当前在构型标准化、复杂环境感知、跨模态协同和大规模调度等方面面临的挑战并提出未来研究方向。研究结果表明：在人工智能、通信与能源技术的推动下，IFC技术正由以构型与动力为主导的单点突破阶段，逐步迈向以智能控制与系统协同为核心的综合集成阶段；将IFC纳入城市综合交通体系，从交通系统层面实现可预测、可管理与可验证的运行闭环，可为构建高效、安全、绿色的未来城市交通体系提供新路径。Abstract: The progress of IFC key technologies was systematically concluded from three major aspects: car body design and power systems, autonomous navigation and control technologies, and vehicle-road-cloud collaboration. The current development of diverse configurations such as ducted fans, folding wings, and modular structures, as well as energy architectures including hybrid power and fuel cell was summarized. Methods of autonomous navigation and control, including multi-sensor fusion, deep learning-based path planning, and robust control were mainly reviewed. The application of low-altitude communication networks, cooperative perception, and intelligent scheduling in integrated vehicle-road-cloud frameworks was discussed. Current challenges of IFC in configuration standardization, complex environment perception, cross-modal collaboration, and large-scale scheduling were analyzed, and potential research directions were proposed. Research results show that, driven by artificial intelligence, communication, and energy technologies, IFC technology is evolving from a single-point breakthrough stage dominated by configuration and power toward a comprehensive integration stage centered on intelligent control and system collaboration. By incorporating IFCs into urban integrated transportation systems, a predictable, manageable, and verifiable operational closed loop can be achieved at the transportation system level. Thus, a new pathway is provided for building efficient, safe, and sustainable future urban transportation systems.
-
表 1 全球科技企业与传统汽车制造商研发的典型UAM产品
Table 1. Typical UAM products developed by global technology companies and traditional automakers
企业名称 产品类型 产品型号 关键技术 应用进展 应用场景 智能化程度 国外 Joby Aviation (美国)[8] eVTOL S4 倾转旋翼构型实现高效平衡高速前飞与垂直起降 计划于2026年初投入运营 城市飞行出租车服务 高水平自主飞行，具备自主起降、航线飞行与避障能力 Archer Aviation (美国)[8] Midnight 12翼布局提高有效载荷，电池支持快充，续航能力优秀 2025年底已在阿联酋开始推进商业化部署 城市飞行出租车服务 侧重高性能飞控与导航 PAL-V (荷兰)[9] IFC Liberty 可折叠旋翼结合三轮车底盘，配备旋翼自转技术保障故障时安全降落 计划于2026年启动生产并于年底交付 城际通勤，适应沙漠环境 飞控系统辅助飞行员决策 Alef Aeronautics (美国)[9] Model A 创新网状结构车身提高气动效率，空中灵活旋转改变飞行模式 计划于2026年初量产交付 面向个人消费者，可在美国空中飞行 支持垂直/水平飞行模式智能切换 Russian Science Foundation (俄罗斯) Cyclo-car 采用圆形风扇构型，机动性强，噪音低 2022年完成缩比原型机测试 军民两用 飞控系统辅助飞行员决策 Klein Vision (斯洛伐克) AirCar 采用可伸缩机翼和折叠尾翼系统，并配备弹道降落伞系统作为安全保障 计划于2026年开始批量生产 城际通勤 飞控系统辅助飞行员决策 中国 亿航智能[8] eVTOL EH216-S 采用分布式动力设计，配备多套飞控系统，关键部件全备份设计，安全冗余充分 2023年商业化试点运营 低空旅游项目 通过地面调度系统管理，可完成群体无人驾驶飞行 小鹏汇天 X2 采用封闭式座舱和全碳纤维结构，机臂可折叠 2024年6月完成首飞 城市内短途空中出行 支持手动和自动驾驶2种模式，可通过简单操作实现沿预定航线飞行 奇瑞汽车 IFC 三体复合翼飞行汽车 模块化分体式设计，可实现陆空形态快速转换 2024年10月完成首飞 城市内短途立体出行 取消传统方向盘和油门踏板，支持无人驾驶 广汽集团 GOVE 飞行舱可与底盘分离并实现精准起飞，底盘可作为共享移动充电站 2023年6月完成首飞，2024年获特许飞行证 城市内短途立体出行 支持飞行及地面行驶2种模式下的无人驾驶，底盘可分离自主寻找充电站 
下载: 导出CSV
表 2 飞行汽车车体构型技术路线分类
Table 2. Classification of technical routes for flying car body configuration
下载: 导出CSV
表 3 飞行汽车动力架构分类
Table 3. Classification of power architecture for flying car
分类 特点 能量效率 参考文献 串联混动 发动机驱动发电机，输出纯电动力，但转换效率较低，电池续航能力差 发动机-发电机-电机传动综合效率约为55%~ 65%，整体能效较低 [33]、[34] 并联混动 发动机与电机并联输出，提高瞬态响应，但系统集成复杂，热管理要求高 能量路径较短，综合效率相较串联结构约高10%~15% [35]~[38] 燃料电池增程 氢燃料电池与电驱耦合，可提升续航能力，但氢能基础设施尚未成熟 燃料电池发电效率为50%~60%，与高效电机耦合后系统效率约为70%~80% [39]~[41] 串并联混合 通过行星齿轮或功率分流装置实现多种能量路径切换，在不同飞行阶段自动优化功率分配，但机构与控制算法复杂 理论综合效率可达75%~85%，但实际效果取决于能量管理策略 [42]、[43]
下载: 导出CSV
表 4 中、美、欧IFC构型与动力系统技术路线对比
Table 4. Comparison of IFC configuration and power system technology routes among China, the USA and Europe
下载: 导出CSV
表 5 无人机避障与路径规划方法对比分析
Table 5. Comparative analysis of obstacle avoidance and path planning methods for unmanned aerial vehicles
方法类型 核心模型 关键算法 实时性 路径质量 适用环境 文献 最优性 平滑度 动力学可行性 基于图搜索 图模型 A*、Dijkstra 中 全局最优 折线，须后处理 差，须后优化 静态、结构化 [58]~[61] 基于采样 空间模型/概率路图模型 RRT*、PRM 中 渐近最优 折线，须后处理 差，须后优化 静态、已知 [62]~[64] 基于数值优化 动力学模型、状态空间模型 MPC、QP 中下 局部最优 高阶光滑 优，显式处理约束 动态、结构化 [65]~[67] 基于元启发式优化 目标函数模型、群体智能模型 GA、PSO、FOA 低 全局近似最优 依赖目标函数设计 中，依赖复杂约束 静态、非结构化 [68]~[70] 基于反应式 几何模型/势场模型 APF、VO、VFH+ 高 局部可行 易发生振荡 中，依赖参数 动态、非结构化 [71]~[73] 基于学习 神经网络模型 DRL (推理阶段)高 数据驱动最优 依赖训练数据质量 中，可约束训练 非结构化、未知 [74]、[75] 混合方法 多模态融合模型 分层规划算法 取决于算法组成 近似全局最优，局部优化调整 通过优化层实现高阶光滑 优，显式处理约束 大规模、动态 [76]~[78]
下载: 导出CSV
表 7 构型差异对智能交通系统研究的影响
Table 7. Impact of configuration differences on intelligent transportation system research
对比维度 一体式 分体式 特征 优势 特征 优势 系统集成与优化 高度集成，软硬件一体化 便于系统整体优化 模块相对独立 便于标准化设计与模块替换 感知系统部署 感知系统集中部署 数据一致性高 感知系统可按功能分布部署 有利于功能冗余与分区优化 控制与导航策略 控制逻辑统一 适合融合式地空控制 支持地空分离控制策略 便于灵活切换与分工 交通系统适应性 交通调度较集中 更适合个人化出行 易接入城市综合交通网络 更适合共享出行与多模式接驳 平台共享能力 车辆专属性强，资源利用率相对较低 支持飞行模块与地面模块共享使用，显著提高使用效率 应用场景匹配 更适用于高端私人通勤、长距离跨城出行 更适合城市内部短途接驳、末端配送、公共/共享出行场景
下载: 导出CSV
-
[1] COHEN A P, SHAHEEN S A, FARRAR E M. Urban air mobility: History, ecosystem, market potential, and challenges[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(9): 6074-6087. doi: 10.1109/TITS.2021.3082767 [2] PAN G F, ALOUINI M S. Flying car transportation system: Advances, techniques, and challenges[J]. IEEE Access, 2021, 9: 24586-24603. doi: 10.1109/ACCESS.2021.3056798 [3] CHOUDHARY C, AASTHA, SAINI G K, et al. Evaluation of potential flying cars[C]//IEEE. 2023 2nd International Conference for Innovation in Technology (INOCON). New York: IEEE, 2023: 1-5. [4] CHEN L C, HUANG C J, CHENG Y S, et al. UAV-enabled video streaming architecture for urban air mobility: A 6G-based approach toward low-altitude 3D transportation[J]. Drones, 2025, 9(6): 448. doi: 10.3390/drones9060448 [5] GARROW L A, GERMAN B J, LEONARD C E. Urban air mobility: A comprehensive review and comparative analysis with autonomous and electric ground transportation for informing future research[J]. Transportation Research Part C: Emerging Technologies, 2021, 132: 103377. doi: 10.1016/j.trc.2021.103377 [6] XIANG S W, XIE A H, YE M X, et al. Autonomouse eVTOL: A summary of researches and challenges[J]. Green Energy and Intelligent Transportation, 2024, 3(1): 100140. doi: 10.1016/j.geits.2023.100140 [7] WENG C Q, PAN T L, CHEN C, et al. Urban low-altitude air transport management: Bridging dynamic traffic control and static network equilibrium[J]. Transportation Research Part C: Emerging Technologies, 2025, 178: 105237. doi: 10.1016/j.trc.2025.105237 [8] 韩玉琪, 朱大明, 付玉, 等. 2022电动垂直起降飞行器主要进展[J]. 航空动力, 2023(1): 19-22.HAN Yu-qi, ZHU Da-ming, FU Yu, et al. The main progress of eVTOLs in 2022[J]. Aerospace Power, 2023(1): 19-22. [9] 马嘉欣, 汪志鸿, 刘卓, 等. 飞行汽车关键技术及应用研究综述[J]. 汽车工程, 2025, 47(11): 2049-2069.MA Jia-xin, WANG Zhi-hong, LIU Zhuo, et al. A review of key technologies and application of flying cars[J]. Automotive Engineering, 2025, 47(11): 2049-2069. [10] 贾圣真, 姚怡行. 面向未来的城市空中交通法律规制框架[J]. 北京航空航天大学学报(社会科学版), 2025, 38(1): 63-70.JIA Sheng-zhen, YAO Yi-xing. Future-oriented legal regulatory framework for urban air mobility[J]. Journal of Beijing University of Aeronautics and Astronautics (Social Sciences Edition), 2025, 38(1): 63-70. [11] SWAMINATHAN N, REDDY S R P, RAJASHEKARA K, et al. Flying cars andeVTOLs: Technology advancements, powertrain architectures, and design[J]. IEEE Transactions on Transportation Electrification, 2022, 8(4): 4105-4117. doi: 10.1109/TTE.2022.3172960 [12] 张新钰, 荣松松, 李骏, 等. 智能飞行汽车关键技术及发展趋势[J]. 中国科学(技术科学), 2024, 54(4): 601-624.ZHANG Xin-yu, RONG Song-song, LI Jun, et al. State-of-the-art and technical trends of intelligent flying cars[J]. Scientia Sinica (Technologica), 2024, 54(4): 601-624. [13] ZHANG T, BARAKOS G N. Review on ducted fans for compound rotorcraft[J]. The Aeronautical Journal, 2020, 124(1277): 941-974. doi: 10.1017/aer.2019.164 [14] LI S, CHEN J X, FAN K, et al. High-integration integrated drive system for distributed propulsion ducted fans[J]. IET Conference Proceedings, 2025, 2024(13): 2143-2147. doi: 10.1049/icp.2024.3364 [15] HAN X, MIYAZAKI R, GAO T H, et al. Large-workspace dual multi-rotor aerial payload deployment system using a cable-suspended device with ducted fans[J]. IEEE Access, 2024, 12: 27029-27038. doi: 10.1109/ACCESS.2024.3366704 [16] HUANG W K, ZHANG Z R, HUANG W X, et al. Optimal design of high speed electric ducted fan for private eVTOL considering specific power and temperature distribution[C]//IEEE. 2021 24th International Conference on Electrical Machines and Systems (ICEMS). New York: IEEE, 2021: 2523-2528. [17] SANTOS D, HICKS J, FERNANDES G D, et al. Computational and experimental aerodynamics of an E-VTOL aircraft with distributed underwing ducted fan propulsion and boundary layer ingestion[C]//AIAA. AIAA Aviation Forum and Ascend 2024. Reston: AIAA, 2024: 4513. [18] MISRA A, JAYACHANDRAN S, KENCHE S, et al. A review on vertical take-off and landing (VTOL) tilt-rotor and tilt wing unmanned aerial vehicles (UAVs)[J]. Journal of Engineering, 2022, 2022: 1803638. [19] LI C X, ZHOU L, QIU T. Design of auxiliary drive mechanism for folding wing structure[C]//SPIE. 2024 Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems. Long Beach: SPIE, 2024: 63. [20] HUANG Y C, GUO X Y, CAO D X. Aerodynamic characteristics of a Z-shaped folding wing[J]. Aerospace, 2023, 10(9): 749. doi: 10.3390/aerospace10090749 [21] ZHANG P, CHEN H, YIN D Z, et al. A novel folding wing design and locking performance analysis[C]//IEEE. 2021 3rd International Symposium on Robotics & Intelligent Manufacturing Technology (ISRIMT). New York: IEEE, 2021: 394-399. [22] CALVENTE L, ACOSTA J A, OLLERO A. Design and manufacture of the wing folding mechanism for a bioinspired ornithopter[C]//IEEE. 2021 Aerial Robotic Systems Physically Interacting with the Environment (AIRPHARO). New York: IEEE, 2021: 1-6. [23] LI S Y, SHEN Y J, LIU B, et al. Aerodynamic design and analysis of an aerial vehicle module for split-type flying cars in urban transportation[J]. Aerospace, 2025, 12(10): 871. doi: 10.3390/aerospace12100871 [24] CHEN T J, YANG Y, ZHENG Z J, et al. Visual guidance technology of flying cars based on multilevel markers and depth[J]. International Journal of Advanced Robotic Systems, 2024, 21(5): 17298806241279608. doi: 10.1177/17298806241279608 [25] QIAN Y P, LUO Y W, HU X Y, et al. Improving the performance of ducted fans for VTOL applications: A review[J]. Science China Technological Sciences, 2022, 65(11): 2521-2541. doi: 10.1007/s11431-021-2110-x [26] MA T L, WANG X S, QIAO N X, et al. A conceptual design and optimization approach for distributed electric propulsion eVTOL aircraft based on ducted-fan wing unit[J]. Aerospace, 2022, 9(11): 690. doi: 10.3390/aerospace9110690 [27] 高舒煜, 李京阳, 王哲, 等. 涵道式飞行汽车设计与气动特性研究[J]. 光学精密工程, 2025, 33(7): 1100-1113.GAO Shu-yu, LI Jing-yang, WANG Zhe, et al. Design and aerodynamic characteristics research of ducted flying car[J]. Optics and Precision Engineering, 2025, 33(7): 1100-1113. [28] 林正航, 李京阳, 宝音贺西. 飞行汽车机翼折叠系统机构运动学分析[J]. 科学技术与工程, 2024, 24(7): 2994-3003.LIN Zheng-hang, LI Jing-yang, BAOYIN He-xi. Kinematic analysis of flying car wing folding system mechanism[J]. Science Technology and Engineering, 2024, 24(7): 2994-3003. [29] CHEN C L, YANG S C, CHEN F. Simulation analysis of aerodynamic characteristics of flying car based on bionic seagull wing[C]//IEEE. 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO). New York: IEEE, 2022: 1301-1306. [30] 付健, 涂良辉, 闫超, 等. 飞行汽车典型构型特征及动力系统架构[J]. 飞行力学, 2025, 43(3): 1-8.FU Jian, TU Liang-hui, YAN Chao, et al. Typical flying car configuration characteristics and power system architecture[J]. Flight Dynamics, 2025, 43(3): 1-8. [31] 王琛, 林威, 胡良鹏, 等. 分体式飞行汽车全自主对接导引系统设计与验证[J]. 浙江大学学报(工学版), 2023, 57(12): 2345-2355.WANG Chen, LIN Wei, HU Liang-peng, et al. Design and verification of autonomous docking guidance system for modular flying vehicle[J]. Journal of Zhejiang University (Engineering Science), 2023, 57(12): 2345-2355. [32] 王琛, 雒特, 惠倩倩, 等. 面向分体式飞行汽车对接锁定的机电系统设计与验证[J]. 吉林大学学报(工学版), 2024, 54(8): 2130-2140.WANG Chen, LUO Te, HUI Qian-qian, et al. Design and verification of electromechanical system for docking and locking of modular flying vehicle[J]. Journal of Jilin University (Engineering and Technology Edition), 2024, 54(8): 2130-2140. [33] PARK M, PARK S H. Sizing method for series hybrid electric propulsion systems in vertical take-off and landing (VTOL) aircraft[J]. Aerospace Science and Technology, 2026, 168: 110745. doi: 10.1016/j.ast.2025.110745 [34] AN S Q, CAI G C, PENG X, et al. The design of improved series hybrid power system based on compound-wing VTOL[J]. Drones, 2024, 8(11): 634. doi: 10.3390/drones8110634 [35] JIMENEZ D, VALENCIA E, HERRERA A, et al. Evaluation of series and parallel hybrid propulsion systems for UAVs implementing distributed propulsion architectures[J]. Aerospace, 2022, 9(2): 63. doi: 10.3390/aerospace9020063 [36] THOMAS G L, MALONE B P, CHAPMAN J W, et al. Electrical energy storage design space exploration for a hybrid-electric six passenger quadrotor[C]//IEEE. 2022 IEEE/AIAA Transportation Electrification Conference and Electric Aircraft Technologies Symposium (ITEC+EATS). New York: IEEE, 2022: 319-324. [37] BAI M L, YANG W J, YAN J Z, et al. Performance analysis of MW-class parallel hybrid-electric regional aircraft using predictive energy management strategy[J]. Green Energy and Intelligent Transportation, 2025: 100361. [38] KURT E, ARABUL A Y, KESKIN ARABUL F, et al. Parallel hybrid propulsion system with integration of designed electric machine for medium altitude long endurance UAV[J]. Heliyon, 2025, 11(4): e42871. doi: 10.1016/j.heliyon.2025.e42871 [39] SOLEYMANI M, MOSTAFAVI V, HEBERT M, et al. Hydrogen propulsion systems for aircraft, a review on recent advances and ongoing challenges[J]. International Journal of Hydrogen Energy, 2024, 91: 137-171. doi: 10.1016/j.ijhydene.2024.10.131 [40] MAZZEO F, DI ILIO G. Fuel cell hybrid electric propulsion system for a lightweight helicopter: Design and performance analysis in urban air mobility scenario[J]. International Journal of Hydrogen Energy, 2024, 50: 891-907. doi: 10.1016/j.ijhydene.2023.09.134 [41] NG W, DATTA A. Hydrogen fuel cells and batteries for electric-vertical takeoff and landing aircraft[J]. Journal of Aircraft, 2019, 56(5): 1765-1782. doi: 10.2514/1.C035218 [42] YU F F, CHEN J J, GAO P N, et al. A review of hybrid-electric propulsion in aviation: Modeling methods, energy management strategies, and future prospects[J]. Aerospace, 2025, 12(10): 895. doi: 10.3390/aerospace12100895 [43] CARDONE L M, PETRONE G, DE ROSA S, et al. Review of the recent developments about the hybrid propelled aircraft[J]. Aerotecnica Missili & Spazio, 2024, 103(1): 17-37. [44] RUAN S M, MA Y, WEI Z C, et al. Data-driven cooperative differential game based energy management strategy for hybrid electric propulsion system of a flying car[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(10): 14770-14784. doi: 10.1109/TITS.2024.3386258 [45] RUAN S M, MA Y, WEI Z C, et al. Hierarchical control strategy for the hybrid electric propulsion system of a flying car with engine start-stop system and dynamic coordination[J]. IEEE Transactions on Transportation Electrification, 2024, 10(3): 4904-4918. doi: 10.1109/TTE.2023.3321832 [46] PANIGRAHI S, KRISHNA Y SS, THONDIYATH A. Design, analysis, and testing of a hybrid VTOL tilt-rotor UAV for increased endurance[J]. Sensors, 2021, 21(18): 5987. doi: 10.3390/s21185987 [47] BORETTI A. Advantages of plug-in hybrid electric vertical take-off and landing aircraft with hydrogen energy storage[J]. International Journal of Hydrogen Energy, 2024, 55: 339-346. doi: 10.1016/j.ijhydene.2023.11.170 [48] KANG J, CHUNG S K. Fuel cell-battery power management system foreVTOL drones[C]//IEEE. 2024 13th International Conference on Renewable Energy Research and Applications (ICRERA). New York: IEEE, 2024: 1731-1735. [49] PENG H, YUAN D L, JIANG K L, et al. PPO-based fluctuation- consumption co-optimized energy management for wind-disturbed fuel cell hybrid electric flying car[J]. Journal of Power Sources, 2025, 647: 237349. doi: 10.1016/j.jpowsour.2025.237349 [50] XIE Y, SAVVARISAL A, TSOURDOS A, et al. Review of hybrid electric powered aircraft, its conceptual design and energy management methodologies[J]. Chinese Journal of Aeronautics, 2021, 34(4): 432-450. doi: 10.1016/j.cja.2020.07.017 [51] 张扬军, 钱煜平, 诸葛伟林, 等. 飞行汽车的研究发展与关键技术[J]. 汽车安全与节能学报, 2020, 11(1): 1-16.ZHANG Yang-jun, QIAN Yu-ping, ZHUGE Wei-lin, et al. Progress and key technologies of flying cars[J]. Journal of Automotive Safety and Energy, 2020, 11(1): 1-16. [52] 鞠孝伟, 龙佳兴, 张凤阁, 等. 电动飞行汽车用推进电机发展现状和研究综述[J]. 电工技术学报, 2025, 40(17): 5402-5421.JU Xiao-wei, LONG Jia-xing, ZHANG Feng-ge, et al. Development status and research overview of propulsion motors for eVTOL[J]. Transactions of China Electrotechnical Society, 2025, 40(17): 5402-5421. [53] PALAIA G, ABU SALEM K, CIPOLLA V, et al. A conceptual design methodology for e-VTOL aircraft for urban air mobility[J]. Applied Sciences, 2021, 11(22): 10815. doi: 10.3390/app112210815 [54] JONASSEN V O, KJØRSVIK N S, BLANKENBERG L E, et al. Aerial hybrid adjustment of LiDAR point clouds, frame images, and linear pushbroom images[J]. Remote Sensing, 2024, 16(17): 3179. doi: 10.3390/rs16173179 [55] 李颖, 王荣煊, 万成麟, 等. 分体式飞行汽车立体环境感知系统设计及试验研究[J]. 机械工程学报, 2024, 60(10): 102-111.LI Ying, WANG Rong-xuan, WAN Cheng-lin, et al. Design and experiment research of 3D environmental perception system for split-type flying vehicle[J]. Journal of Mechanical Engineering, 2024, 60(10): 102-111. [56] ZAIDI A, KAZIM M, ZHANG L X, et al. Deep neural network based secured control of flying vehicle in urban environment[C]//IEEE. 2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH). New York: IEEE, 2022: 172-177. [57] MUGHAL M H, KHOKHAR M J, SHAHZAD M. Assisting UAV localization via deep contextual image matching[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 2445-2457. doi: 10.1109/JSTARS.2021.3054832 [58] 祝文杰, 李维, 王子炎. 改进A*算法的无人机城市低空物流路径规划[J/OL]. 计算机工程与应用, 2025, https://link.cnki.net/urlid/11.2127.TP.20250610.1915.012 .ZHU Wen-jie, LI Wei, WANG Zi-yan. Improved A* algorithm for UAV path planning in urban low-altitude logistics[J]. Computer Engineering and Applications, 2025,https://link.cnki.net/urlid/11.2127.TP.20250610.1915.012 .[59] YOO Y D, MOON J H. Study on A-star algorithm-based 3D path optimization method considering density of obstacles[J]. Aerospace, 2025, 12(2): 85. doi: 10.3390/aerospace12020085 [60] FARID G, COCUZZA S, YOUNAS T, et al. Modified A-star (A*) approach to plan the motion of a quadrotor UAV in three-dimensional obstacle-cluttered environment[J]. Applied Sciences, 2022, 12(12): 5791. doi: 10.3390/app12125791 [61] 申晨, 杨磊, 任帅里, 等. 基于启发式A*算法的城市低空无人机航迹规划[J]. 中国民航飞行学院学报, 2025, 36(2): 5-9.SHEN Chen, YANG Lei, REN Shuai-li, et al. Urban low altitude UAV trajectory planning based on heuristic A* algorithm[J]. Journal of Civil Aviation Flight University of China, 2025, 36(2): 5-9. [62] ZHU X Y, GAO Y F, LI Y Y, et al. Fast dynamic P-RRT*-based UAV path planning and trajectory tracking control under dense obstacles[J]. Actuators, 2025, 14(5): 211. doi: 10.3390/act14050211 [63] BIAN P, FAN J M, LIU Y, et al. Quad rapidly-exploring random tree star algorithm with improved potential force for unmanned aerial vehicle path planning[J]. IEEE Access, 2025, 13: 111187-111198. doi: 10.1109/ACCESS.2025.3582959 [64] LI W M, WANG L, ZOU A W, et al. Path planning for UAV based on improved PRM[J]. Energies, 2022, 15(19): 7267. doi: 10.3390/en15197267 [65] CAI Z H, WANG L H, ZHAO J, et al. Virtual target guidance- based distributed model predictive control for formation control of multiple UAVs[J]. Chinese Journal of Aeronautics, 2020, 33(3): 1037-1056. doi: 10.1016/j.cja.2019.07.016 [66] 王晓海, 孟秀云, 李传旭. 基于MPC的无人机航迹跟踪控制器设计[J]. 系统工程与电子技术, 2021, 43(1): 191-198.WANG Xiao-hai, MENG Xiu-yun, LI Chuan-xu. Design of trajectory tracking controller for UAV based on MPC[J]. Systems Engineering and Electronics, 2021, 43(1): 191-198. [67] KOMIYAMA S, UCHIYAMA K, MASUDA K. Combined robust control for quadrotor UAV using model predictive control and super-twisting algorithm[J]. Drones, 2025, 9(8): 576. doi: 10.3390/drones9080576 [68] 赵畅, 刘允刚, 陈琳, 等. 面向元启发式算法的多无人机路径规划现状与展望[J]. 控制与决策, 2022, 37(5): 1102-1115.ZHAO Chang, LIU Yun-gang, CHEN Lin, et al. Research and development trend of multi-UAV path planning based on metaheuristic algorithm[J]. Control and Decision, 2022, 37(5): 1102-1115. [69] LI S J, LEI H T, ZHU C, et al. Bearing-only passive localization and optimized adjustment for UAV formations under electromagnetic silence[J]. Drones, 2025, 9(11): 767. doi: 10.3390/drones9110767 [70] LI L K, FU Y S, YU K, et al. Optimal trajectory UAV path design based on bezier curves with multi-hop cluster selection in wireless networks[J]. Wireless Networks, 2024, 30(6): 5021-5032. doi: 10.1007/s11276-022-03208-1 [71] BAI T T, TANG W, LIU Y, et al. UAV formation cooperative obstacle avoidance based on improved APF method under variable topology[J]. Science China Technological Sciences, 2025, 68(9): 1920403. doi: 10.1007/s11431-024-2961-6 [72] 吴学礼, 史思远, 宋凯, 等. 基于改进VO算法与动态窗口法的自主避障[J]. 科学技术与工程, 2024, 24(14): 5928-5934.WU Xue-li, SHI Si-yuan, SONG Kai, et al. Autonomous obstacle avoidance based on improved VO algorithm and dynamic window method[J]. Science Technology and Engineering, 2024, 24(14): 5928-5934. [73] FU X W, ZHI C Y, WU D. Obstacle avoidance and collision avoidance of UAV swarm based on improved VFH algorithm and information sharing strategy[J]. Computers & Industrial Engineering, 2023, 186: 109761. [74] GUO T, JIANG N, LI B Y, et al. UAV navigation in high dynamic environments: A deep reinforcement learning approach[J]. Chinese Journal of Aeronautics, 2021, 34(2): 479-489. doi: 10.1016/j.cja.2020.05.011 [75] YANG W J, LI B. Connectivity-aware UAV mobility in cellular networks: DRL path planning and predictive handover[J]. Ad Hoc Networks, 2025, 179: 103999. doi: 10.1016/j.adhoc.2025.103999 [76] WU C L, GUO Z Y, ZHANG J, et al. Cooperative path planning for multiple UAVs based on APF B-RRT* algorithm[J]. Drones, 2025, 9(3): 177. doi: 10.3390/drones9030177 [77] 鲜斌, 宋宁. 基于模型预测控制与改进人工势场法的多无人机路径规划[J]. 控制与决策, 2024, 39(7): 2133-2141.XIAN Bin, SONG Ning. A multiple UAVs path planning method based on model predictive control and improved artificial potential field[J]. Control and Decision, 2024, 39(7): 2133-2141. [78] LIU L H, RU L, WANG W F, et al. UAV path planning in threat environment: A*-APF algorithm for spatio-temporal grid optimization[J]. Drones, 2025, 9(9): 661. doi: 10.3390/drones9090661 [79] KITAHARA S, NARUKAWA N, OGAWA K, et al. Beam scanning three-dimensional monopulse antenna for flying cars collision avoidance[C]//IEEE. 2019 IEEE Asia-Pacific Microwave Conference (APMC). New York: IEEE, 2019: 452-454. [80] WEI H L, LOU B C, ZHANG Z Z, et al. Autonomous navigation for eVTOL: Review and future perspectives[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9(2): 4145-4171. doi: 10.1109/TIV.2024.3352613 [81] WU S Z, LIANG X, FANG Y C, et al. Geometric maneuvering for underactuated VTOL vehicles[J]. IEEE Transactions on Automatic Control, 2024, 69(3): 1507-1519. doi: 10.1109/TAC.2023.3324268 [82] ZHAO J, YANG C, WANG W D, et al. An improved elitist-Q- Learning path planning strategy for VTOL air-ground vehicle using convolutional neural network mode prediction[J]. Advanced Engineering Informatics, 2025, 65: 103316. doi: 10.1016/j.aei.2025.103316 [83] XU J, LU X J, LUO W, et al. Dynamic modeling and altitude control for flying cars based on active disturbance rejection control[J]. Applied Sciences, 2024, 14(7): 2754. doi: 10.3390/app14072754 [84] FAN L J, LI D L, REN L J, et al. Constraint-following robust control for flying car: A model-based approach[C]//IEEE. 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C). New York: IEEE, 2024: 962-969. [85] 郄天琪, 王伟达, 杨超, 等. 面向分体式飞行汽车自主对接的自动驾驶底盘运动规划方法研究[J]. 机械工程学报, 2024, 60(10): 235-244.QIE Tian-qi, WANG Wei-da, YANG Chao, et al. Motion planning method of autonomous driving chassis for autonomous docking of the split-type flying vehicle[J]. Journal of Mechanical Engineering, 2024, 60(10): 235-244. [86] XIN L, ZHOU G T, YU Z Y, et al. Vehicle-road-cloud collaborative perception: Resource and intelligence optimization[J]. Applied Sciences, 2025, 15(23): 12613. doi: 10.3390/app152312613 [87] 廖小罕, 徐晨晨, 叶虎平, 等. 无人机应用发展关键基础设施与低空公共航路网规划[J]. 中国科学院院刊, 2022, 37(7): 977-988.LIAO Xiao-han, XU Chen-chen, YE Hu-ping, et al. Critical infrastructures for developing UAVs' applications and low-altitude public air-route network planning[J]. Bulletin of Chinese Academy of Sciences, 2022, 37(7): 977-988. [88] HELKA-LIINA MÄÄTTÄNEN R. 3GPP standardization for cellular-supported UAVs[J]. UAV Communications for 5G and Beyond, 2021: 163-180. [89] LIN X Q. An overview of 5G advanced evolution in 3GPP release 18[J]. IEEE Communications Standards Magazine, 2022, 6(3): 77-83. doi: 10.1109/MCOMSTD.0001.2200001 [90] ARANI A H, HU P, ZHU Y Y. UAV-assisted space-air-ground integrated networks: A technical review of recent learning algorithms[J]. IEEE Open Journal of Vehicular Technology, 2024, 5: 1004-1023. doi: 10.1109/OJVT.2024.3434486 [91] ZHANG P Y, CHEN S P, ZHENG X G, et al. UAV communication in space-air-ground integrated networks (SAGINs): Technologies, applications, and challenges[J]. Drones, 2025, 9(2): 108. doi: 10.3390/drones9020108 [92] VANELLI-CORALLI A, CHUBERRE N, MASINI G, et al. Non-terrestrial networks overview[M]//VANELLI-CORALLI A, CHUBERRE N, MASINI G, et al. 5G Non-Terrestrial Networks: Technologies, Standards, and System Design. New York: Wiley-IEEE Press, 2024: 53-90. [93] CHENG N, HE J C, YIN Z S, et al. 6G service-oriented space-air-ground integrated network: A survey[J]. Chinese Journal of Aeronautics, 2022, 35(9): 1-18. [94] YANG K, WANG Y C, GAO X Z, et al. Communications in space-air-ground integrated networks: An overview[J]. Space: Science & Technology, 2025, 5: 199. [95] ARYENDU I, ARYA S, WANG Y. GeTOA: Game-theoretic optimization for AOI of ultra-reliable eVTOL collaborative communication[C]//IEEE. 2024 IEEE Wireless Communications and Networking Conference (WCNC). New York: IEEE, 2024: 1-7. [96] NDIKUMANA A, NGUYEN K K, CHERIET M. 5G open RAN-based network slicing for connecting ground-based and flying cars serving urban areas[C]//IEEE. ICC 2024 - IEEE International Conference on Communications. New York: IEEE, 2024: 1764-1769. [97] 王煜婷, 冷甦鹏, 熊凯. 面向高动态城市空中交通网络的多径智能传输策略[J]. 电信科学, 2025, 41(3): 64-72.WANG Yu-ting, LENG Su-peng, XIONG Kai. Multipath intelligent transmission strategy for UAM dynamic networks[J]. Telecommunications Science, 2025, 41(3): 64-72. [98] HÄCKEL T, VON ROENN L, JUCHMANN N, et al. Coordinating cooperative perception in urban air mobility for enhanced environmental awareness[C]//IEEE. 2024 International Conference on Unmanned Aircraft Systems (ICUAS). New York: IEEE, 2024: 1026-1033. [99] DAI W, PANG B Z, LOW K H. Conflict-free four-dimensional path planning for urban air mobility considering airspace occupancy[J]. Aerospace Science and Technology, 2021, 119: 107154. doi: 10.1016/j.ast.2021.107154 [100] HABA R, MANO T, UEDA R, et al. Routing and scheduling optimization for urban air mobility fleet management using quantum annealing[J]. Scientific Reports, 2025, 15: 4326. doi: 10.1038/s41598-025-86843-w [101] XIONG B K, JIANG R, WANG K, et al. Utilizing eVTOL aircraft to alleviate traffic congestion on an arterial road[J]. IEEE Transactions on Intelligent Transportation Systems, 2025, 26(7): 10415-10427. doi: 10.1109/TITS.2025.3553817 [102] 毕军, 回晶, 成沛璇, 等. 机坪货运保障人员配置计划优化方法[J]. 交通运输工程学报, 2025, 25(4): 254-266. doi: 10.19818/j.cnki.1671-1637.2025.04.018BI Jun, HUI Jing, CHENG Pei-xuan, et al. Optimization method of personnel allocation plan for apron cargo support[J]. Journal of Traffic and Transportation Engineering, 2025, 25(4): 254-266. doi: 10.19818/j.cnki.1671-1637.2025.04.018 [103] 尹浩东, 沈燕, 屈姝含, 等. 城市飞行汽车停机坪选址问题研究二: 面向轴辐式网络的多目标优化方法[J]. 交通运输工程与信息学报, 2025, 23(3): 88-102.YIN Hao-dong, SHEN Yan, QU Shu-han, et al. Locating urban flying car vertiports part Ⅱ: Two-stage optimization for hub-and-spoke networks[J]. Journal of Transportation Engineering and Information, 2025, 23(3): 88-102. [104] 肖建力, 邱雪, 张扬, 等. 交通大模型综述[J]. 交通运输工程学报, 2025, 25(1): 8-28. doi: 10.19818/j.cnki.1671-1637.2025.01.002 XIAO Jian-li, QIU Xue, ZHANG Yang, et al. Review on large language models in transportation[J]. Journal of Traffic and Transportation Engineering, 2025, 25(1): 8-28. doi: 10.19818/j.cnki.1671-1637.2025.01.002 [105] 刘文学, 侯聪, 杨亚联, 等. 面向城市空中交通的电动飞行汽车关键性能指标分析[J]. 机械工程学报, 2024, 60(22): 257-275.LIU Wen-xue, HOU Cong, YANG Ya-lian, et al. Analysis of key performance metrics of electric flying cars for urban air mobility[J]. Journal of Mechanical Engineering, 2024, 60(22): 257-275. [106] 郭江涛, 孙蕊, 陈含智, 等. 协同因子辅助下GNSS/IMU/BA组合的低空机载导航算法[J/OL]. 北京航空航天大学学报, 2025, https://doi.org/10.13700/j.bh.1001-5965.2025.0167 .GUO Jiang-tao, SUN Rui, CHEN Han-zhi, et al. Low-altitude airborne navigation algorithm based on GNSS/IMU/BA combination assisted by synergy factor[J/OL]. Journal of Beijing University of Aeronautics and Astronautics, 2025,https://doi.org/10.13700/j.bh.1001-5965.2025.0167 .[107] 吴婉晴, 蔚保国, 董赫. 基于城市低空环境的多域态势融合算法研究[J/OL]. 无线电工程, 2025, https://link.cnki.net/urlid/13.1097.TN.20250702.1008.005 .WU Wan-qing, YU Bao-guo, DONG He. Research on multi-domain situation fusion algorithm based on geographic and electromagnetic data[J/OL]. Radio Engineering, 2025,https://link.cnki.net/urlid/13.1097.TN.20250702.1008.005 .[108] SHU Y B, FAN S R, FENG X L. Research on UAV 3D path planning with improved A-star algorithm[C]//IEEE. 2024 17th International Conference on Advanced Computer Theory and Engineering (ICACTE). New York: IEEE, 2024: 334-338. [109] 李平伟, 张剑高, 张财, 等. 基于改进A*算法的低空多无人机路径规划方法研究[J]. 交通科学与工程, 2025, 41(4): 61-68.LI Ping-wei, ZHANG Jian-gao, ZHANG Cai, et al. Low-altitude multi-UAV path planning method based on improved A*algorithm[J]. Journal of Transport Science and Engineering, 2025, 41(4): 61-68. [110] 冯棣坤, 张洪海, 华明壮, 等. 面向城市低空物流的多层异质起降场点网络协同规划[J]. 交通运输工程学报, 2026, 26(2): 110-124. doi: 10.19818/j.cnki.1671-1637.2026.086FENG Di-kun, ZHANG Hong-hai, HUA Ming-zhuang, et al. Multi-layer heterogeneous take-off and landing site network collaborative planning for urban low-altitude logistics[J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 110-124. doi: 10.19818/j.cnki.1671-1637.2026.086 [111] PARK C, PARK S, KIM G S, et al. Multi-agent deep reinforcement learning for efficient passenger delivery in urban air mobility[C]//IEEE. ICC 2023 - IEEE International Conference on Communications. New York: IEEE, 2023: 5689-5694. [112] ZHUANG J F, HAN G F, XIA Z H, et al. Robust policy learning for multi-UAV collision avoidance with causal feature selection[J]. Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2025, 1: 2392-2401. [113] RESNIK D B, ANDREWS S L. A precautionary approach to autonomous vehicles[J]. AI and Ethics, 2024, 4(2): 403-418. doi: 10.1007/s43681-023-00277-6 [114] HE L, AOUF N, SONG B F. Explainable deep reinforcement learning for UAV autonomous path planning[J]. Aerospace Science and Technology, 2021, 118: 107052. doi: 10.1016/j.ast.2021.107052 [115] DISSANAYAKA D, WANASINGHE T R, GOSINE R G. Explainable artificial intelligence for autonomous UAV navigation[C]//IEEE. 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: IEEE, 2024: 10439-10446. [116] 权在昕, 武丁杰, 高嘉静, 等. 城市低空空中交通及无人机路径规划研究综述[J]. 航空计算技术, 2024, 54(2): 121-126.QUAN Zai-xin, WU Ding-jie, GAO Jia-jing, et al. Overview of urban low altitude air mobility and UAV path planning[J]. Aeronautical Computing Technique, 2024, 54(2): 121-126. [117] LI X B, PATEL S, BVSKENS C. Let hybrid A* path planner obey traffic rules: A deep reinforcement learning- based planning framework[C]//IEEE. 2025 11th International Conference on Automation, Robotics, and Applications (ICARA). New York: IEEE, 2025: 404-411. [118] ZHONG L S, ZHAO J S, LUO H N, et al. Hybrid path planning and following of a quadrotor UAV based on deep reinforcement learning[C]//IEEE. 2024 36th Chinese Control and Decision Conference (CCDC). New York: IEEE, 2024: 1858-1863. [119] 马新辉.低空智联网空天地协同技术研究——关键挑战与对策建议[J/OL].无线电工程, 2025, https://link.cnki.net/urlid/13.1097.TN.20250609.0823.002 .MA Xin-hui. Air-space-ground collaborative technologies for low-altitude intelligent networks: Key challenges and strategic recommendations[J/OL]. Radio Engineering, 2025,https://link.cnki.net/urlid/13.1097.TN.20250609.0823.002 .[120] KAVAS-TORRIS O, GELBAL S Y, CANTAS M R, et al. V2X communication between connected and automated vehicles (CAVs) and unmanned aerial vehicles (UAVs)[J]. Sensors, 2022, 22(22): 8941. doi: 10.3390/s22228941 [121] JIN Y Z, LIU X G, ZHU Q. DSRC & C-V2X comparison for connected and automated vehicles in different traffic scenarios[EB/OL]. ArXiv, 2022, https://arxiv.org/abs/2203.12553 .[122] GU X H, ZHANG G A. A survey on UAV-assisted wireless communications: Recent advances and future trends[J]. Computer Communications, 2023, 208: 44-78. doi: 10.1016/j.comcom.2023.05.013 [123] LIN S J, LI L Y, CHEN J N, et al. IATS: Information-age aware task scheduling for vehicle-road-cloud cooperative systems[J]. Journal of Systems Architecture, 2025, 167: 103480. doi: 10.1016/j.sysarc.2025.103480 [124] 张洪海, 夷珈, 李姗, 等. 低空空域容量评估研究综述[J]. 交通运输工程学报, 2023, 23(6): 78-93. doi: 10.19818/j.cnki.1671-1637.2023.06.003ZHANG Hong-hai, YI Jia, LI Shan, et al. Review on research of low-altitude airspace capacity evaluation[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 78-93. doi: 10.19818/j.cnki.1671-1637.2023.06.003 [125] 孔得建, 袁泽. 低空经济政策法律体系的现状、经验与展望[J]. 北京航空航天大学学报(社会科学版), 2024, 37(5): 85-95.KONG De-jian, YUAN Ze. Current situation, experience and prospect of the policies and legal system of low-altitude economy[J]. Journal of Beijing University of Aeronautics and Astronautics (Social Sciences Edition), 2024, 37(5): 85-95. -
图(3) / 表(7)
计量
- 文章访问数: 32
- HTML全文浏览量: 23
- PDF下载量: 6
- 被引次数: 0
