Intelligent Unmanned Aerial Vehicles for Low-altitude Economy: 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、无人机物流和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。该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、公共安全和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4大领域的关键技术内涵；归纳低空无人机通信3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，并详细分析了智能反射面(IRS)辅助的非正交多址接入(NOMA)通信、自组网拓扑优化和移动边缘计算分别在3类网络中的核心作用。解析了无人机在可靠通信、智能感知、自主协同和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 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. -
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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