Intelligent UAVs for Advanced Air Mobility: A Review of the Technology Framework and Future Prospects
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摘要: 随着新质生产力与数字经济的深度发展，低空经济作为融合通用航空、无人机物流、空中出行等形态的新型产业体系，正成为全球经济增长的新引擎。无人机凭借其高性价比、可扩展性与高度智能化，在其中扮演着核心赋能者角色。本文系统性梳理并构建了面向低空经济的智能无人机技术体系，该体系遵循从底层基础到顶层应用的逻辑，通过通信网络贯通“感知-决策-行动”闭环，总结了无人机在物流运输、城市空中交通、公共安全、工业巡检等典型场景中的应用模式。剖析了其在感知与定位、通信与组网、智能决策与控制及空域集成与安全四大领域的关键技术内涵；归纳低空无人机通信三大关键网络类型，即无人机与蜂窝网络深度融合网络、无人机自组织专用网络、无人机计算应用网络，并详细分析了IRS辅助的NOMA通信、自组网拓扑优化和移动边缘计算分别在三类网络中的核心作用。解析了无人机在可靠通信、智能感知、自主协同、能源动力等方面面临的技术挑战以及在空域管理、法规标准、商业模式与社会接受度方面的非技术挑战。展望智能全域通信、认知群体智能、高置信度自主安全及绿色可持续技术等未来融合发展趋势的同时，提出基于“挑战驱动-技术融合-体系构建-反馈迭代”的低空经济无人系统技术闭环演进范式，揭示了其发展内在逻辑是以应用为导向、具备自我优化能力的动态递归过程。Abstract:
Significance The deep integration of new quality productive forces and the digital economy has catalyzed the low-altitude economy into a pivotal new engine for global economic growth. Operating within airspace typically below 3000 meters, this novel industrial system encompasses applications from UAV logistics and Urban Air Mobility (UAM) to industrial inspection and public safety. Intelligent Unmanned Aerial Vehicles (UAV), characterized by their cost-effectiveness, scalability, and autonomy, serve as the core enabler in this ecosystem. They are driving a paradigm shift in aviation from traditional, centralized models towards distributed, intelligent, and service-oriented aerial utilization. The strategic significance of UAVs extends to fostering industrial transformation, enhancing urban infrastructure, safeguarding national airspace security, and stimulating regional economic growth. Consequently, a systematic review and construction of the intelligent UAV technology framework is essential to guide future research, address critical challenges, and unlock the full potential of this transformative sector.Progress This paper systematically constructs a holistic technology framework for intelligent UAV, structured from foundational to application layers. The framework integrates four interconnected domains. Intelligent Perception and Navigation focus on robust operation in complex settings, utilizing tight-coupled multi-sensor fusion and advanced state estimation algorithms like visual-inertial odometry, augmented by multi-source adaptive positioning in GNSS-denied environments. Wireless Communication Networks prioritize reliable BVLOS links through cellular network integration, self-organizing FANETs with intelligent topology control, and UAV-enhanced edge computing for optimized resource allocation. Autonomous Decision-Making and Cooperative Control has evolved from classical methods to learning-based approaches, with Multi-Agent Reinforcement Learning enabling sophisticated swarm behaviors and dynamic task execution. Finally, Low-Altitude Security and Airspace Management establishes essential infrastructure through integrated detection and countermeasure systems, complemented by UAV Cloud and UTM platforms for coordinated airspace operations. Conclusions The review conclusively demonstrates the evolution of UAV from isolated platforms to networked, intelligent nodes within a broader low-altitude economy system. Despite significant progress across all technological domains, substantial challenges persist. Key technical bottlenecks include ensuring communication reliability in unique low-altitude channels, overcoming perception limitations in complex and deceptive environments, achieving resilient autonomous collaboration under uncertainty, and breaking through the inherent constraints of current energy and power systems. These are compounded by significant non-technical hurdles, including the development of adaptive regulatory and airspace integration frameworks, viable business models for mass-scale applications, and achieving broad public acceptance. The analysis affirms that overcoming these barriers necessitates the deep fusion of enabling technologies. A "challenge-driven → technology fusion → system construction → feedback iteration" closed-loop evolution paradigm is proposed, which aptly captures the intrinsic, recursive logic of technological advancement in this field and provides a valuable framework for guiding future academic and industrial efforts. Prospects Looking ahead, the development of intelligent UAV technology is poised to focus on several highly synergistic frontiers. The evolution towards Intelligent Holistic Communication will see the creation of a proactive "data field" through the deep integration of air-ground-space networks and Integrated Sensing and Communication (ISAC), enabling predictive resource allocation and robust connectivity. Cognitive Swarm Intelligence will transform UAV swarms into collaborative cognitive entities by integrating large language models for task understanding and Multi-Agent Reinforcement Learning for decentralized, adaptive decision-making, allowing for emergent intelligent behaviors. The critical issue of High-Assurance Autonomous Security will be addressed through rigorous formal verification of AI models, explainable AI for transparent decision-making, and the extensive use of digital twins for virtual testing and certification, building the trust required for widespread deployment. Finally, a focus on Green Sustainable Technologies will permeate the entire lifecycle, driving innovations in high-energy-density power sources like solid-state and hydrogen fuel cells, the adoption of eco-friendly materials, and AI-optimized operations for minimal energy consumption and noise impact, ensuring the long-term viability and social harmony of the low-altitude economy. -
表 1 低空经济中无人机的典型应用场景与技术总结
应用场景 描述 关键技术 商业模式 面临挑战 代表案例/试点 低空物流与运输 快递配送、医疗急救物资运输、跨海湾/山区运输等，强调高效、低成本的货物移动 自主导航与路径规划、电池与
动力管理、感知与避障、货物
装载/卸载系统、通信链路B2B(如物流公司合作)、B2C(直接配送服务) 空域管理、电池续航、天气适应性、法规合规 美团无人机配送(深圳)、Zipline(卢旺达/美国) 医疗物资空投项目 城市空中
交通聚焦eVTOL与载人级无人机的运营，实现城市内短途客运
或货运垂直起降、轻量化材料、
电动推进系统、安全与认证
标准、空中交通管理共享出行服务、城市交通网络
集成安全性认证、
噪音控制、
基础设施建设亿航EH216-S、Joby Aviation(美国) 城市
空中交通测试项目公共安全与应急响应 应用于消防救援、警务巡逻、
灾害勘测与救援，提升响应
速度与情景感知能力实时视频传输、热成像与多光谱传感器、多机协同控制、快速部署机制、数据融合分析 政府采购、公共服务合作 应急通信可靠性、恶劣环境
适应性、数据
隐私与安全深圳“医疗急救走廊”
无人机送血项目、澳大利亚西太平洋海域“无人机
救援”项目工业巡检与农林牧渔 涵盖电力/油气管道巡检、
大型基础设施监测、精准农业，
优化资源利用与维护效率多光谱/LiDAR传感、自动巡检算法、数据分析与AI、长效
续航技术、高精度定位订阅服务(如应
用监测)、项目
合同(工业巡检)数据准确性、操作自动化程度、成本效益平衡 浙江电网无人机智能巡检项目、极飞科技农业植保
无人机
下载: 导出CSV
表 2 无人机集群协同任务分配算法对比描述
典型算法 算法类别 算法描述 算法特征 穷举遍历
分支定界集中式优化类 构建最优化数学模型进行求解 问题表述程度有限、计算处理过程复杂、中心化依赖严重 粒子群算法
遗传算法集中式启发类 不断调节解质量，进行启发式优化 解算结果随机化程度过高、缺乏严格数学理论支撑、
中心化依赖严重、算法实现容易市场拍卖 分布式模拟类 将复杂任务映射至某场景，借助其运行方式解决问题 扩展性强、实现灵活、信息同步程度要求高 人工智能 分布式/集中式
马尔可夫决策将分配转移为动态决策解决问题 动态决策能力强、环境适应程度高、求解过程时间跨度长 群智能算法 分布式生物类 基于局部动态感知与变化，形成组织行为解决问题 解算结果随机化程度过高、缺乏严格数学理论支撑、
规模变化不敏感、算法实现容易
下载: 导出CSV
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