Vision-Language Navigation for Aerial Robots: Towards the Era of Large Language Models
Defense Innovation Institute, Chinese Academy of Military Sciences（军事科学院国防创新研究院）；Intelligent Game and Decision Laboratory（智能博弈与决策实验室）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Xingyu Xia, Lekai Zhou, Yujie Tang, Xiaozhou Zhu, Hai Zhu, Wen Yao
Aerial vision-and-language navigation (Aerial VLN) aims to enable unmanned aerial vehicles (UAVs) to interpret natural language instructions and autonomously navigate complex three-dimensional environments by grounding language in visual perception. This survey provides a critical and analytical review of the Aerial VLN field, with particular attention to the recent integration of large language models (LLMs) and vision-language models (VLMs). We first formally introduce the Aerial VLN problem and define two interaction paradigms: single-instruction and dialog-based, as foundational axes. We then organize the body of Aerial VLN methods into a taxonomy of five architectural categories: sequence-to-sequence and attention-based methods, end-to-end LLM/VLM methods, hierarchical methods, multi-agent methods, and dialog-based navigation methods. For each category, we systematically analyze design rationales, technical trade-offs, and reported performance. We critically assess the evaluation infrastructure for Aerial VLN, including datasets, simulation platforms, and metrics, and identify their gaps in scale, environmental diversity, real-world grounding, and metric coverage. We consolidate cross-method comparisons on shared benchmarks and analyze key architectural trade-offs, including discrete versus continuous actions, end-to-end versus hierarchical designs, and the simulation-to-reality gap. Finally, we synthesize seven concrete open problems: long-horizon instruction grounding, viewpoint robustness, scalable spatial representation, continuous 6-DoF action execution, onboard deployment, benchmark standardization, and multi-UAV swarm navigation, with specific research directions grounded in the evidence presented throughout the survey.
School of Automation Science and Engineering, South China University of Technology（华南理工大学自动化科学与工程学院）；State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所机器人重点实验室）；State Key Laboratory of Robotics and System, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）机械工程与自动化学院机器人与系统重点实验室）
ImagineUAV: Aerial Vision-Language Navigation via World-Action Modeling and Kinodynamic Planning
Pengcheng Laboratory（鹏城实验室）；School of Computer Science and Cyber Engineering（计算机科学与网络工程学院）；Guangzhou University（广州大学）；Southern University of Science and Technology（南方科技大学）
Deep Learning-based Robust Autonomous Navigation of Aerial Robots in Dense Forests
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland（遥感与摄影测量系，芬兰地理空间研究 institute FGI，芬兰国家土地测绘局）
ViVa-SAFELAND: a New Freeware for Safe Validation of Vision-based Navigation in Aerial Vehicles
Center for Research in Mathematics CIMAT（数学研究中心（CIMAT））；Centro de Investigación y de Estudios Avanzados del IPN, CINVESTAV-IPN（墨西哥理工学院高级研究中心（CINVESTAV-IPN））
OnFly: Onboard Zero-Shot Aerial Vision-Language Navigation toward Safety and Efficiency
School of Artificial Intelligence, Sun Yat-Sen University（中山大学人工智能学院）；Southern University of Science and Technology（南方科技大学）；The Hong Kong University of Science and Technology（香港科技大学）
Fly0: Persistent Metric Anchoring for Zero-Shot Aerial Vision-Language Navigation
National Key Laboratory of Big Data and Decision（大数据与决策国家重点实验室）；National University of Defense Technology（国防科技大学）；State Key Laboratory of Digital Intelligent Modeling and Simulation（数字智能建模与仿真国家重点实验室）；Information Support Force Engineering University（信息支援力量工程大学）
Openfly: A comprehensive platform for aerial vision-language navigation
Shanghai AI Laboratory（上海人工智能实验室）；Northwestern Polytechnical University（西北工业大学）；Beihang University（北航）；Shanghai Jiao Tong University（上海交通大学）；The University of Hong Kong（香港大学）；Zhejiang University（浙江大学）；University of Science and Technology of China（中国科学技术大学）；East China University of Science and Technology（东华大学）；Fudan University（复旦大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；TeleAI
School of Electrical Engineering and Computer Science, The University of Queensland（昆士兰大学电气工程与计算机科学学院）；CSIRO Robotics, Data61（联邦科学与工业研究组织机器人学与Data61研究院）
RINGO: Real-time Navigation with a Guiding Trajectory for Aerial Manipulators in Unknown Environments
Institute of Robotics and Automatic Information System（机器人与自动信息系统研究所）；College of Artificial Intelligence（人工智能学院）；Tianjin Key Laboratory of Intelligent Robotics（天津市智能机器人重点实验室）；Nankai University（南开大学）；Institute of Intelligence Technology and Robotic Systems（智能技术与机器人系统研究所）；Shenzhen Research Institute of Nankai University（南开大学深圳研究院）
GeoNav: Empowering MLLMs with dual-scale geospatial reasoning for language-goal aerial navigation
College of Systems Engineering, National University of Defense Technology（系统工程学院，国防科技大学）；State Key Laboratory of Digital Intelligent Modeling and Simulation（数字智能建模与仿真国家重点实验室）；BNRist, Tsinghua University（清华大学BNRist）
Agile in the Face of Delay: Asynchronous End-to-End Learning for Real-World Aerial Navigation
School of Intelligence and Engineering（智能与工程学院）；Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics（广东省智能变形机制与自适应机器人重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）
Vision-based Navigation of Unmanned Aerial Vehicles in Orchards: An Imitation Learning Approach
Department of Biological and Agricultural Engineering, University of California, Davis, Davis, CA, USA（生物与农业工程系，加州大学戴维斯分校）；Department of Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA, USA（机械与航空航天工程系，加州大学戴维斯分校）
Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Deep Reinforcement Learning Through Environmental Generalization
Universidad Tecnológica del Uruguay (UTEC)（乌拉圭技术大学）；Centro de Ciências Computacionais (C3) of Universidade Federal do Rio Grande (FURG)（里奥格兰德联邦大学计算科学中心）；Universidade Federal de Santa Maria (UFSM)（圣玛丽亚联邦大学）
