AeroVerse-Review: Comprehensive survey on aerial embodied vision-and-language navigation

Fanglong Yao; Youzhi Liu; Wenyi Zhang; Zhengqiu Zhu; Chenglong Li; Nayu Liu; Peng Hu; Yuanchang Yue; Kaiwen Wei; Xin He; Xudong Zhao; Zihan Wei; Haotian Xu; Zhiyuan Wang; Gujie Shao; Liu Yang; Dan Zhao; Yong Yang

doi:10.59717/j.xinn-inform.2025.100015

The Innovation Informatics > 2025 Vol. 1 > No. 1 > 100015

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AeroVerse-Review: Comprehensive survey on aerial embodied vision-and-language navigation

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100190, China
3.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
4.
Key Laboratory of Target Cognition and Application Technology (TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
5.
State Key Laboratory of Digital Intelligent Modeling and Simulation, National University of Defense Technology, Changsha 410073, China
6.
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
7.
Flight Technology College, Civil Aviation Flight University of China, Chengdu 641400, China
8.
Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support, Chengdu 641400, China
9.
Tianjin Key Laboratory Autonomous Intelligence Technology and Systems, School of Computer Science and Technology, Tiangong University, Tianjin 300387, China
10.
School of Computer Science and Engineering, Beihang University, Beijing 100191, China
11.
College of Computer Science, Chongqing University, Chongqing 400044, China
12.
Information and Navigation School, Air Force Engineering University, Xi'an 710077, China
13.
School of Software, University of Science and Technology of China, Suzhou 215123, China
14.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
15.
Institute of Computing Technology, China Academy of Railway Sciences, Beijing 100081, China
16.
Institute of Scientific and Technical Information of China, Beijing 100038, China
17.
These authors contributed equally

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Corresponding author: yaofanglong17@mails.ucas.ac.cn
Received Date: September 21, 2025

Accepted Date: November 04, 2025

Published Online: November 07, 2025

The Innovation Informatics 1(1), https://doi.org/10.59717/j.xinn-inform.2025.100015 | Cite this article

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. Comprehensive review of unmanned aerial vehicle based vision-and-language navigation (UAV-VLN).
  
  Provide a summary of methodological evolution, from single-UAV navigation to multi-agent collaboration.
  
  Outline promising future directions, such as embodied world models and space-air-ground unmanned systems.

ABSTRACT

ABSTRACT

With the rapid advancement of unmanned aerial vehicle (UAV) technology, embedding intelligence into aerial platforms has become an increasingly important research direction. UAV-based vision-and-language navigation (UAV-VLN), as a representative paradigm of aerospace embodied intelligence, requires UAVs to understand natural language instructions and integrate multimodal perception to autonomously plan and execute navigation tasks in three-dimensional environments. This survey provides a comprehensive review of UAV-VLN research, covering simulation platforms, task definitions, core methodologies, datasets and evaluation metrics, application scenarios, as well as key challenges and future directions. We first present the design principles and capabilities of mainstream simulators, followed by a structured summary of methodological progress, including rule-based approaches, deep learning-driven models, and multi-agent collaborative strategies. We then discuss critical technical challenges in UAV-VLN, such as dynamic feasibility and control in 3D space, perception and generalization in complex environments, linguistic ambiguity and cross-modal semantic grounding, long-term spatiotemporal reasoning, and deployment under resource constraints. Based on these challenges, we outline promising future directions, including standardized benchmark development, Sim-to-Real and cross-domain transfer, pretrained large model integration, embodied world model, collaborative and interactive UAV-VLN, and embodied navigation of space-air-ground unmanned systems. This survey aims to provide a structured reference for future research and to guide the practical deployment of UAV-VLN systems.
- Unmanned Aerial Vehicle (UAV) Agent /
- Simulation Platforms /
- UAV-based Vision-and-Language Navigation /
- Aerospace Embodied Intelligence

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REFERENCES

[1]	Yao F., Yue Y., Liu Y., et al. (2024). Aeroverse: Uav-agent benchmark suite for simulating, pre-training, finetuning, and evaluating aerospace embodied world models. arXiv preprint. DOI:10.48550/arXiv.2408.15511 View in Article Google Scholar
[2]	Liu Y., Yao F., Yue Y., et al (2024). Navagent: Multi-scale urban street view fusion for uav embodied vision-and-language navigation. arXiv preprint. DOI:10.48550/arXiv.2411.08579 View in Article Google Scholar
[3]	Li J. and Yang S.X. (2025). Digital twins to embodied artificial intelligence: Review and perspective. Intell. Robot. 5:202−227. DOI:10.20517/ir.2025.11 View in Article CrossRef Google Scholar
[4]	Sarkar A., Narasimhan S.S., Singh A.P., et al. (2024). Gomaa-geo: Goal modality agnostic active geo-localization. arXiv preprint. DOI:10.48550/arXiv.2406.01917 View in Article Google Scholar
[5]	Liu J., Cui J., Ye M., et al. (2024). Shooting condition insensitive unmanned aerial vehicle object detection. Expert Syst. Appl. 246:123221. DOI:10.1016/j.eswa.2023.123221 View in Article CrossRef Google Scholar
[6]	Qiu H., Li J., Gan J., et al. (2024). Dronegpt: Zero-shot video question answering for drones. CVDL '24. 64:1–6. DOI: 10.1145/3653804.3654608 View in Article Google Scholar
[7]	Chen G. (2023). Typefly: Flying drones with large language model. arXiv preprint. DOI:10.48550/arXiv.2312.14950 View in Article Google Scholar
[8]	Kondo K. (2023). Real: Resilience and adaptation using large language models on autonomous aerial robots. arXiv preprint. DOI:10.48550/arXiv.2311.01403 View in Article Google Scholar
[9]	Long X., Zhao Q., Zhang K., et al. (2025). A survey: Learning embodied intelligence from physical simulators and world models. arXiv preprint. DOI:10.48550/arXiv.2507.00917 View in Article Google Scholar
[10]	DJI. (2022). Dji drone solutions for inspection and infrastructure construction in the oil and gas industry. https://enterprise.dji.com/cn/oil-and-gas View in Article Google Scholar
[11]	DJI. (2022). Dji drone solutions for optimizing operations in the public safety industry. https://enterprise.dji.com/cn/public-safety View in Article Google Scholar
[12]	DJI. (2022). Dji drone solutions for surveying, urban planning, aec, and natural resource management. https://enterprise.dji.com/cn/surveying View in Article Google Scholar
[13]	Anderson P., Wu Q., Teney D., et al. (2018). Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments. Comp. Vis. Pattern Recogn.2018:3674–3683. DOI:10.1109/CVPR.2018.00387 View in Article Google Scholar
[14]	Qi Y., Wu Q., Anderson P., et al. (2020). Reverie: Remote embodied visual referring expression in real indoor environments. Comp. Vis. Pattern Recogn.2020:9975–9984. DOI:10.1109/CVPR42600.2020.01000 View in Article Google Scholar
[15]	Jain V., Magalhaes G., Ku A., et al. (2019). Stay on the path: Instruction fidelity in vision-and-language navigation. Assoc. Comput. Ling.1: 1867–1876. DOI:10.18653/v1/P19-1181 View in Article Google Scholar
[16]	Ku A., Anderson P., Patel R., et al. (2020). Room-across-room: Multilingual vision-and-language navigation with dense spatiotemporal grounding. Empir. Methods Nat. Lang. Process. 2020:4392–4412. DOI: 10.18653/v1/2020.emnlp-main.355 View in Article Google Scholar
[17]	Li C., Xia F., Martín-Martín R., et al. (2021). Igibson 2.0: Object-centric simulation for robot learning of everyday household tasks. Conf. Robot Learn.155:455–465. DOI: 10.48550/arXiv.2108.03272 View in Article Google Scholar
[18]	Shen B., Xia F., Li C., et al. (2021). Igibson 1.0: A simulation environment for interactive tasks in large realistic scenes. Int. Conf. Intell. Robots Syst. 2021:7520–7527. DOI:10.1109/IROS51168.2021.9636509 View in Article Google Scholar
[19]	Xia F., Shen W.B., Li C., et al. (2020). Interactive gibson benchmark: A benchmark for interactive navigation in cluttered environments. IEEE Robot. Autom. Lett. 5:713−720. DOI:10.1109/LRA.2020.2965079 View in Article CrossRef Google Scholar
[20]	Huang Y., Chen J. and Huang D. (2022). Ufpmp-det: Toward accurate and efficient object detection on drone imagery. AAAI Conf. Artif. Intell. 36:1026−1033. DOI:10.1609/aaai.v36i1.19986 View in Article CrossRef Google Scholar
[21]	Zhu X., Lyu S., Wang X., et al. (2021). Tph-yolov5: Improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios. IEEE Int. Conf. Comput. Vis. 2021:2778–2788. DOI:10.1109/ICCVW54120.2021.00312 View in Article Google Scholar
[22]	Lin F. (2025). Uavs meet llms: Overviews and perspectives toward agentic low-altitude mobility. Inf. Fusion. 122:103158. DOI:10.1016/j.inffus.2025.103158 View in Article CrossRef Google Scholar
[23]	Hao W., Li C., Lee P., et al. (2020). Towards learning a generic agent for vision-and-language navigation via pre-training. Comp. Vis. Pattern Recogn. 2020:13134–13143. DOI:10.1109/CVPR42600.2020.01315 View in Article Google Scholar
[24]	Ke L., Li X., Huang B., et al. (2019). Tactical rewind: Self-correction via backtracking in vision-and-language navigation. Comp. Vis. Pattern Recogn. 2019:6734–6743. DOI:10.1109/CVPR.2019.00689 View in Article Google Scholar
[25]	Qi Y., Pan Z., Zhang S., et al. (2020). Object-and-action aware model for visual language navigation. Eur. Conf. Comput. Vis. 2020:303–317. DOI:10.1007/978-3-030-58598-3_19 View in Article Google Scholar
[26]	Chattopadhyay P., Hoffman J., Parikh D., et al. (2021). Robustnav: Towards benchmarking robustness in embodied navigation. Int. Conf. Comput. Vis. 2021:15671–15680. DOI:10.1109/ICCV48922.2021.01539 View in Article Google Scholar
[27]	Chen S., Guhur P.L., Tapaswi M., et al. (2022). Think global, act local: Dual-scale graph transformer for vision-and-language navigation. Comp. Vis. Pattern Recogn. 2022:16516–16526. DOI:10.1109/CVPR52688.2022.01607 View in Article Google Scholar
[28]	Shah S., Dey D., Lovett C., et al. (2018). Airsim: High-fidelity visual and physical simulation for autonomous vehicles. Field Serv. Robot. 2018:621–635. DOI:10.1007/978-3-319-67361-5_40 View in Article Google Scholar
[29]	Lee J., Miyanishi T., Kurita S., et al. (2024). Citynav: Language-goal aerial navigation dataset with geographic information. Int. Conf. Comput. Vis. 2024:1–12. DOI:10.48550/arXiv.2406.14240 View in Article Google Scholar
[30]	Liu S., Zhang H., Qi Y., et al. (2023). Aerialvln: Vision-and-language navigation for uavs. arXiv preprint. DOI:10.48550/arXiv.2308.06735 View in Article Google Scholar
[31]	Gao Y., Li C., You Z., et al. (2025). Openfly: A versatile toolchain and large-scale benchmark for aerial vision-language navigation. arXiv preprint. DOI:10.48550/arXiv.2502.18041 View in Article Google Scholar
[32]	Wu Y. (2025). Learning occlusion-robust vision transformers for real-time uav tracking. arXiv preprint. DOI:10.48550/arXiv.2504.09228 View in Article Google Scholar
[33]	Wang Z., Li J. and Mahmoudian N. (2024). Synergistic reinforcement and imitation learning for vision-driven autonomous flight of uav along river. Int. Conf. Intell. Robots Syst. 2024:9976–9982. DOI:10.1109/IROS47612.2024.10611488 View in Article Google Scholar
[34]	Wang X., Huang Q., Celikyilmaz A., et al. (2019). Reinforced cross-modal matching and self-supervised imitation learning for vision-language navigation. Comp. Vis. Pattern Recogn. 2019:6629–6638. DOI:10.1109/CVPR.2019.00679 View in Article Google Scholar
[35]	Sautenkov O. (2025). Uav-codeagents: Scalable uav mission planning via multi-agent react and vision-language reasoning. arXiv preprint. DOI:10.48550/arXiv.2505.07236 View in Article Google Scholar
[36]	Zhou G. (2023). Navgpt: Explicit reasoning in vision-and-language navigation with large language models. arXiv preprint. DOI:10.48550/arXiv.2305.16986 View in Article Google Scholar
[37]	Kong L. (2025). Eventfly: Event camera perception from ground to the sky. arXiv preprint. DOI:10.48550/arXiv.2503.19916 View in Article Google Scholar
[38]	Wang H., Wang W., Liang W., et al. (2021). Structured scene memory for vision-language navigation. Comp. Vis. Pattern Recogn. 2021:8455–8464. DOI:10.1109/CVPR46437.2021.00846 View in Article Google Scholar
[39]	Anderson P., Shrivastava A., Parikh D., et al. (2019). Chasing ghosts: Instruction following as bayesian state tracking. Adv. Neural Inf. Process. Syst. 2019:371–381. DOI:10.48550/arXiv.1907.02022 View in Article Google Scholar
[40]	Zhou Z., Zhang Z., Peng X., et al. (2023). Decentralized cooperative navigation method for multi-uavs based on memory fusion mode. Acta Aeronaut. Astronaut. Sin. 44:28440. DOI:10.7527/S10006893.2023.28440 View in Article CrossRef Google Scholar
[41]	Yuan S. (2025). Airswarm: Enabling cost-effective multi-uav research with cots drones. arXiv preprint. DOI:10.48550/arXiv.2503.06890 View in Article Google Scholar
[42]	Chen J. (2025). Aeroduo: Aerial duo for uav-based vision and language navigation. ACM Int. Conf. Multimedia. 2025:1–10. DOI:10.48550/arXiv.2508.15232 View in Article Google Scholar
[43]	Zhang Y., Hu Y., Song Y., et al. (2025). Learning vision-based agile flight via differentiable physics. Nat. Mach. Intell. 7:954–966. DOI:10.1038/s42256-025-01048-0 View in Article Google Scholar
[44]	Liao Y., Su J., Ma D., et al. (2025). Uav-satellite cross-view image matching based on adaptive threshold-guided ring partitioning framework. Remote Sens. 17:2448. DOI:10.3390/rs17142448 View in Article CrossRef Google Scholar
[45]	Gong N., Li L., Sha J., et al. (2024). A satellite-drone image cross-view geolocalization method based on multi-scale information and dual-channel attention mechanism. Remote Sens. 16:941. DOI:10.3390/rs16060941 View in Article CrossRef Google Scholar
[46]	Sautenkov O. (2025). Uav-vlpa: A vision-language-path-action system for optimal route generation on a large scales. arXiv preprint. DOI:10.48550/arXiv.2503.02454 View in Article Google Scholar
[47]	Anderson P., Wu Q., Teney D., et al. (2018). Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments. Comp. Vis. Pattern Recogn. 2018:3674–3683. DOI:10.1109/CVPR.2018.00387 View in Article Google Scholar
[48]	Jain V., Magalhaes G., Ku A., et al. (2019). Stay on the path: Instruction fidelity in vision-and-language navigation. Assoc. Comput. Ling. 2019:1867–1876. DOI:10.18653/v1/P19-1181 View in Article Google Scholar
[49]	Chen H., Suhr A., Misra D., et al. (2019). Touchdown: Natural language navigation and spatial reasoning in visual street environments. Comp. Vis. Pattern Recogn. 2019:12538–12547. DOI:10.1109/CVPR.2019.01281 View in Article Google Scholar
[50]	Hong Y., Wu Q., Qi Y., et al. (2020). Language and visual entity relationship graph for agent reasoning in vision-and-language navigation. Adv. Neural Inf. Process. Syst. 644:7685–7696. DOI:10.48550/arXiv.2010.09304 View in Article Google Scholar
[51]	Tan M. and Le Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. Int. Conf. Mach. Learn. 2019:6105–6114. DOI:10.48550/arXiv.1905.11946 View in Article Google Scholar
[52]	Dosovitskiy A., Beyer L., Kolesnikov A., et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint. DOI:10.48550/arXiv.2010.11929 View in Article Google Scholar
[53]	Mnih V., Kavukcuoglu K., Silver D., et al. (2015). Human-level control through deep reinforcement learning. Nature. 518:529−533. DOI:10.1038/nature14236 View in Article CrossRef Google Scholar
[54]	Schulman J. (2017). Proximal policy optimization algorithms. arXiv preprint. DOI:10.48550/arXiv.1707.06347 View in Article Google Scholar
[55]	Haarnoja T., Zhou A., Abbeel P., et al. (2018). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. Int. Conf. Mach. Learn. 2018:1861–1870. DOI: 10.48550/arXiv.1801.01290 View in Article Google Scholar
[56]	Ross S., Gordon G. and Bagnell D. (2011). A reduction of imitation learning and structured prediction to no-regret online learning. Artif. Intell. Stat. 2011:627–635. DOI: 10.48550/arXiv.1011.0686 View in Article Google Scholar
[57]	Chen T., Kornblith S., Norouzi M., et al. (2020). A simple framework for contrastive learning of visual representations. Int. Conf. Mach. Learn. 2020:1597–1607. DOI: 10.48550/arXiv.2002.05709 View in Article Google Scholar
[58]	He K., Fan H., Wu Y., et al. (2020). Momentum contrast for unsupervised visual representation learning. arXiv preprint. DOI:10.48550/arXiv.1911.05722 View in Article Google Scholar
[59]	Kipf T.N. and Welling M. (2017). Semi-supervised classification with graph convolutional networks. arXiv preprint. DOI: 10.48550/arXiv.1609.02907. View in Article Google Scholar
[60]	Yang Y., Li Y., Song W., et al. (2018). Graph r-cnn for scene graph generation. Eur. Conf. Comput. Vis. 2018:690–706. DOI:10.1007/978-3-030-01246-5_41 View in Article Google Scholar
[61]	Hu R., Rohrbach A., Darrell T., et al. (2020). Language-conditioned graph networks for relational reasoning. Int. Conf. Comput. Vis. 2020:10294–10303. DOI:10.1109/ICCV.2019.01039 View in Article Google Scholar
[62]	Bonetto E. (2023). Grade: Generating realistic and dynamic environments for robotics research with isaac sim. arXiv preprint. DOI:10.48550/arXiv.2303.04466 View in Article Google Scholar
[63]	Handa A. (2021). Isaac gym: High performance gpu-based physics simulation for robot learning. arXiv preprint. DOI:10.48550/arXiv.2108.10470 View in Article Google Scholar
[64]	Richards A. and How J. (2002). Robust model predictive control for autonomous unmanned rotorcraft. AIAA Guid. Navig. Control Conf. 3:1936−1941. View in Article Google Scholar
[65]	Forster C., Pizzoli M. and Scaramuzza D. (2014). Svo: Fast semi-direct monocular visual odometry. Int. Conf. Robot. Autom. 2014:15−22. DOI:10.1109/ICRA.2014.6906584 View in Article CrossRef Google Scholar
[66]	LaValle S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Tech. Rep. Comput. Sci. Dep. Iowa State Univ. View in Article Google Scholar
[67]	Karaman S. and Frazzoli E. (2011). Sampling-based algorithms for optimal motion planning. Robot. Sci. Syst. arXiv preprint. DOI: 10.1177/0278364911406761 View in Article Google Scholar
[68]	Mellinger D. and Kumar V. (2011). Minimum snap trajectory generation and control for quadrotors. Int. Conf. Robot. Autom. 2011:2520−2525. DOI:10.1109/ICRA.2011.5980409 View in Article CrossRef Google Scholar
[69]	Kamel M., Burri M. and Siegwart R. (2017). Linear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehicles. IFAC World Congr. 50:3463−3469. DOI:10.1016/j.ifacol.2017.08.926 View in Article CrossRef Google Scholar
[70]	Mur-Artal R. and Tardós J.D. (2017). Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Trans. Robot. 33:1255−1262. DOI:10.1109/TRO.2017.2705103 View in Article CrossRef Google Scholar
[71]	Hart P.E., Nilsson N.J. and Raphael B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4:100−107. DOI:10.1109/TSSC.1968.300136 View in Article CrossRef Google Scholar
[72]	Kamel M., Stastny T., Alexis K., et al. (2017). Linear vs non-linear mpc for trajectory tracking applied to rotary wing micro aerial vehicles. IFAC World Congr. 50: 3463–3469. DOI: 10.1016/j.ifacol.2017.08.927 View in Article Google Scholar
[73]	Mayne D.Q. (2014). Model predictive control: Recent developments and future promise. Automatica. 50:2967−2986. DOI:10.1016/j.automatica.2014.10.128 View in Article CrossRef Google Scholar
[74]	Vaswani A., Shazeer N., Parmar N., et al. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst. 30:5998–6008. DOI: 10.48550/arXiv.1706.03762 View in Article Google Scholar
[75]	He K., Zhang X., Ren S., et al. (2016). Deep residual learning for image recognition. Comp. Vis. Pattern Recogn. 2016: 770–778. DOI:10.1109/CVPR.2016.90 View in Article Google Scholar
[76]	Peng X.B., Andrychowicz M., Zaremba W., et al. (2018). Sim-to-real transfer of robotic control with dynamics randomization. Int. Conf. Robot. Autom. 2018:3803–3810. DOI:10.1109/ICRA.2018.8460528 View in Article Google Scholar
[77]	Tassa Y., Doron Y., Muldal A., et al. (2018). Deepmind control suite. arXiv preprint. DOI:10.48550/arXiv.1801.00690 View in Article Google Scholar
[78]	Wu S.T. and Hong J.L. (2010). Bayesian relational memory for semantic visual navigation. Int. Conf. Comput. Vis. 2010: 2769–2779. DOI:10.1109/ICCV.2019.00286 View in Article Google Scholar
[79]	Loquercio A., Maqueda A.I., Del-Blanco C.R., et al. (2018). Dronet: Learning to fly by driving. IEEE Robot. Autom. Lett. 3:1088−1095. DOI:10.1109/LRA.2018.2795645 View in Article CrossRef Google Scholar
[80]	Tai L., Paolo G. and Liu M. (2017). Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation. Int. Conf. Intell. Robots Syst. 2017:31–36. DOI:10.1109/IROS.2017.8202134 View in Article Google Scholar
[81]	Bekmezci I., Sahingoz O.K. and Temel O. (2013). Flying ad-hoc networks (fanets): A survey. Ad Hoc Netw. 11:1254−1270. DOI:10.1016/j.adhoc.2012.12.004 View in Article CrossRef Google Scholar
[82]	Brambilla M., Ferrante E., Birattari M., et al. (2013). Swarm robotics: A review from the swarm engineering perspective. Swarm Intell. 7:1−41. DOI:10.1007/s11721-012-0075-2 View in Article CrossRef Google Scholar
[83]	Howard A., Zhu M., Chen B., et al. (2020). Multi-uav coordination for large-scale area coverage: A survey and taxonomy. Robot. Auton. Syst. 125:103400. DOI:10.1016/j.robot.2020.103400 View in Article CrossRef Google Scholar
[84]	Chen J. (2025). Aeroduo: Aerial duo for uav-based vision and language navigation. arXiv preprint. DOI:10.48550/arXiv.2508.15232 View in Article Google Scholar
[85]	Radford A., Kim J.W., Hallacy C., et al. (2021). Learning transferable visual models from natural language supervision. arXiv preprint. DOI:10.48550/arXiv.2103.00020 View in Article Google Scholar
[86]	Dosovitskiy A., Beyer L., Kolesnikov A., et al. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint. DOI: 10.48550/arXiv.2010.11929 View in Article Google Scholar
[87]	Liu Z., Lin Y., Cao Y., et al. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. Int. Conf. Comput. Vis. 2021:10012–10022. DOI:10.1109/ICCV48922.2021.00986 View in Article Google Scholar
[88]	Radford A., Wu J., Child R., et al. (2019). Language models are unsupervised multitask learners. OpenAI Blog. 1:9.https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf View in Article Google Scholar
[89]	Yan L. (2023). Vlm-nav: Vision-language models for uav navigation. arXiv preprint.DOI:10.48550/arXiv.2310.02971 View in Article Google Scholar
[90]	Bouabdallah S. (2007). Design and control of quadrotors with application to autonomous flying. PhD Thesis, EPFL. DOI:10.5075/epfl-thesis-3727 View in Article Google Scholar
[91]	Hoffmann G.M., Huang H., Waslander S.L., et al. (2007). Quadrotor helicopter flight dynamics and control: Theory and experiment. AIAA Guid. Navig. Control Conf 2007:6461. DOI: 10.2514/6.2007-6461 View in Article Google Scholar
[92]	Mahony R., Kumar V. and Corke P. (2012). Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor. IEEE Robot. Autom. Mag. 19:20−32. DOI:10.1109/MRA.2012.2205474 View in Article CrossRef Google Scholar
[93]	Faessler M., Franchi A. and Scaramuzza D. (2018). Differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking control. IEEE Robot. Autom. Lett. 3:620−626. DOI:10.1109/LRA.2017.2776355 View in Article CrossRef Google Scholar
[94]	Tobin J., Fong R., Ray A., et al. (2017). Domain randomization for transferring deep neural networks from simulation to the real world. Int. Conf. Intell. Robots Syst. 2019:23–30. DOI:10.1109/IROS.2017.8202133 View in Article Google Scholar
[95]	Peng X.B., Andrychowicz M., Zaremba W., et al. (2018). Sim-to-real transfer of robotic control with dynamics randomization. arXiv preprint. DOI: 10.48550/arXiv.1710.06537 View in Article Google Scholar
[96]	Hinton G., Vinyals O. and Dean J. (2015). Distilling the knowledge in a neural network. arXiv preprint. DOI: 10.48550/arXiv.1503.02531 View in Article Google Scholar
[97]	Sandler M., Howard A., Zhu M., et al. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. Comp. Vis. Pattern Recogn. 4510–4520. DOI:10.1109/CVPR.2018.00474 View in Article Google Scholar
[98]	Redmon J. and Farhadi A. (2018). Yolov3: An incremental improvement. arXiv preprint. DOI:10.48550/arXiv.1804.02767 View in Article Google Scholar
[99]	NVIDIA Corporation. (2022). Nvidia isaac sim. https://developer.nvidia.com/isaac-sim View in Article Google Scholar
[100]	Epic Games. (2022). Unreal engine. https://www.unrealengine.com View in Article Google Scholar
[101]	Koenig N. and Howard A. (2004). Design and use paradigms for gazebo, an open-source multi-robot simulator. Int. Conf. Intell. Robots Syst. 2004:2149–2154. DOI:10.1109/IROS.2004.1389727 View in Article Google Scholar
[102]	Gorelick N., Hancher M., Dixon M., et al. (2017). Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202:18−27. DOI:10.1016/j.rse.2017.06.031 View in Article CrossRef Google Scholar
[103]	Ji Y., He B., Tan Z., et al. (2025). Game4loc: A uav geo-localization benchmark from game data. AAAI Conf. Artif. Intell. 39:3913−3921. DOI:10.1609/aaai.v39i4.32409 View in Article CrossRef Google Scholar
[104]	Rohmer E., Singh S.P.N. and Freese M. (2013). Coppeliasim (formerly v-rep): A versatile and scalable robot simulation framework. Int. Conf. Intell. Robots Syst 2013:1321-1326. DOI:10.1109/IROS.2013.6696520 View in Article Google Scholar
[105]	Michel O. (2004). Webots: Professional mobile robot simulation. Int. J. Adv. Robot. Syst. 1:39−42. DOI:10.5772/5618 View in Article CrossRef Google Scholar
[106]	Echeverria G., Lemaignan S., Degroote A., et al. (2011). Modular open robots simulation engine: Morse. Int. Conf. Robot. Autom. 2011:46–51. DOI:10.1109/ICRA.2011.5980355 View in Article Google Scholar
[107]	Carpin S., Lewis M., Wang J., et al. (2007). Usarsim: A robot simulator for research and education. Int. Conf. Robot. Autom. 2007:1400–1405. DOI:10.1109/ROBOT.2007.363204 View in Article Google Scholar
[108]	Panerati J. and Schoellig A.P. (2021). Learning to fly—a gym environment with pybullet physics for reinforcement learning of multi-agent quadcopter control. Int. Conf. Intell. Robots Syst. 2021:7512–7519. DOI:10.1109/IROS51168.2021.9636161 View in Article Google Scholar
[109]	Kshitij. (2018). Drone simulation with realistic controls. https://github.com/Kshitij08/Drone-Simulation View in Article Google Scholar
[110]	Fan Y., Chen W., Jiang T., et al. (2023). Aerial vision-and-dialog navigation. Assoc. Comput. Ling. 2023:3043–3061. DOI: 10.18653/v1/2023.findings-acl.190 View in Article Google Scholar
[111]	Gao C., Zhao B., Zhang W., et al. (2024). Embodiedcity: A benchmark platform for embodied agent in real-world city environment. arXiv preprint. DOI:10.48550/arXiv.2410.09604 View in Article Google Scholar
[112]	Lin L., Liu Y., Hu Y., et al. (2022). Capturing, reconstructing, and simulating: The urbanscene3d dataset. Eur. Conf. Comput. Vis. 2022:93–109. DOI:10.1007/978-3-031-20056-4_6 View in Article Google Scholar
[113]	Schmittle M., Lukina A., Vacek L., et al. (2018). Openuav: A uav testbed for the cps and robotics community. Int. Conf. Cyber-Phys. Syst. 2018:130–139. DOI:10.1109/ICCPS.2018.00019 View in Article Google Scholar
[114]	Zhong F. (2024). Unrealzoo: Enriching photo-realistic virtual worlds for embodied ai. arXiv preprint. DOI:10.48550/arXiv.2412.20977 View in Article Google Scholar
[115]	Long R. (2025). Embodied crowd counting. arXiv preprint. DOI:10.48550/arXiv.2503.08367 View in Article Google Scholar
[116]	Wang H. (2024). Grutopia: Dream general robots in a city at scale. arXiv preprint. DOI:10.48550/arXiv.2407.10943 View in Article Google Scholar
[117]	Wu W. (2024). Metaurban: An embodied ai simulation platform for urban micromobility. arXiv preprint. DOI:10.48550/arXiv.2407.08725 View in Article Google Scholar
[118]	Xu Z., Deng D., Dong Y., et al. (2022). Dpmpc-planner: A real-time uav trajectory planning framework for complex static environments with dynamic obstacles. Int. Conf. Robot. Autom. 2022:250–256. DOI:10.1109/ICRA46639.2022.9812165 View in Article Google Scholar
[119]	Mourikis A.I. and Roumeliotis S.I. (2007). A multi-state constraint kalman filter for vision-aided inertial navigation. Int. Conf. Robot. Autom. 2007:3565–3572. DOI:10.1109/ROBOT.2007.364024 View in Article Google Scholar
[120]	Oleynikova H., Burri M., Taylor Z., et al. (2016). Continuous-time trajectory optimization for online uav replanning. Int. Conf. Intell. Robots Syst. 2016:5332–5339. DOI:10.1109/IROS.2016.7759774 View in Article Google Scholar
[121]	Mohta K., Sun K., Liu S., et al. (2018). Experiments in fast, autonomous, gps-denied quadrotor flight. IEEE Int. Conf. Robot. Autom. 2018:7832–7839. DOI:10.1109/ICRA.2018.8461076 View in Article Google Scholar
[122]	Ren Y., Zhu F., Lu G., et al. (2025). Safety-assured high-speed navigation for mavs. Sci. Robot. 10:eado6187. DOI:10.1126/scirobotics.ado6187 View in Article CrossRef Google Scholar
[123]	Xu W., Cai Y., He D., et al. (2022). Fast-lio2: Fast direct lidar-inertial odometry. IEEE Trans. Robot. 38:2053−2073. DOI:10.1109/TRO.2022.3141876 View in Article CrossRef Google Scholar
[124]	Cao S., Lu X. and Shen S. (2022). Gvins: Tightly coupled gnss–visual–inertial fusion for smooth and consistent state estimation. IEEE Trans. Robot. 38:2004−2021. DOI:10.1109/TRO.2022.3148900 View in Article CrossRef Google Scholar
[125]	Hui Y., Chen X., Xu S., et al. (1998). An unmanned air vehicle (uav) gps location and navigation system. Int. Conf. Microwave Millim. Wave Technol. 1998:472–475. DOI:10.1109/ICMMT.1998.768328 View in Article Google Scholar
[126]	Williamson W.R., Rios T. and Speyer J.L. (1999). Carrier phase differential gps/ins positioning for formation flight. Am. Control Conf. 5:3665−3670 DOI:10.1109/ACC.1999.786276 View in Article CrossRef Google Scholar
[127]	Williamson W.R., Abdel-Hafez M.F., Rhee I., et al. (2007). An instrumentation system applied to formation flight. IEEE Trans. Control Syst. Technol. 15:75−85. DOI:10.1109/TCST.2006.883336 View in Article CrossRef Google Scholar
[128]	Pany T., Akos D., Arribas J., et al. (2024). Gnss software-defined radio: History, current developments, and standardization efforts. Navigation. 71. DOI:10.33012/navi.628 View in Article Google Scholar
[129]	Mohta K., Watterson M., Mulgaonkar Y., et al. (2018). Fast, autonomous flight in gps-denied and cluttered environments. J. Field Robot. 35:101−120. DOI:10.1002/rob.21774 View in Article CrossRef Google Scholar
[130]	Forster C., Zhang Z., Gassner M., et al. (2017). Svo: Semidirect visual odometry for monocular and multicamera systems. IEEE Trans. Robot. 33:249−265. DOI:10.1109/TRO.2016.2623335 View in Article CrossRef Google Scholar
[131]	Wang Q., Zheng C. and Wu P. (2022). Geomagnetic/inertial navigation integrated matching navigation method. Heliyon. 8:e11249. DOI:10.1016/j.heliyon.2022.e11249 View in Article CrossRef Google Scholar
[132]	Fang Z., Yang S., Jain S., et al. (2017). Robust autonomous flight in constrained and visually degraded shipboard environments. J. Field Robot. 34:25−52. DOI:10.1002/rob.21673 View in Article CrossRef Google Scholar
[133]	Oleynikova H., Taylor Z., Fehr M., et al. (2017). Voxblox: Incremental 3d euclidean signed distance fields for on-board mav planning. IEEE/RSJ Int. Conf. Intell. Robots Syst. 2017:1366–1373. DOI:10.1109/IROS.2017.8202315 View in Article Google Scholar
[134]	Oleynikova H., Honegger D. and Pollefeys M. (2015). Reactive avoidance using embedded stereo vision for mav flight. IEEE Int. Conf. Robot. Autom. 2015:50–56. DOI:10.1109/ICRA.2015.7138992 View in Article Google Scholar
[135]	Lin J., Zhu H. and Alonso-Mora J. (2020). Robust vision-based obstacle avoidance for micro aerial vehicles in dynamic environments. IEEE Int. Conf. Robot. Autom. 2020:2680–2686. DOI:10.1109/ICRA40945.2020.9196975. View in Article Google Scholar
[136]	Wang Y., Ji J., Wang Q., et al. (2021). Autonomous flights in dynamic environments with onboard vision. IEEE/RSJ Int. Conf. Intell. Robots Syst. 2021:1966–1973. DOI:10.1109/IROS51168.2021.9636005 View in Article Google Scholar
[137]	Zhou B., Gao F., Wang L., et al. (2019). Robust and efficient quadrotor trajectory generation for fast autonomous flight. IEEE Robot. Autom. Lett. 4:3529−3536. DOI:10.1109/LRA.2019.2926660 View in Article CrossRef Google Scholar
[138]	Zhou X., Wang Z., Xu C., et al. (2021). Ego-planner: An esdf-free gradient-based local planner for quadrotors. IEEE Robot. Autom. Lett. 6:478−485. DOI:10.1109/LRA.2020.3047704 View in Article CrossRef Google Scholar
[139]	Xu Z., Xiu Y., Lyu X., et al. (2023). Vision-aided uav navigation and dynamic obstacle avoidance using gradient-based b-spline trajectory optimization. IEEE Int. Conf. Robot. Autom. 2023:1214–1220. DOI:10.1109/ICRA48891.2023.10160899 View in Article Google Scholar
[140]	Liu J.Y., Guo Z.Q. and Liu S.Y. (2012). The simulation of the uav collision avoidance based on the artificial potential field method. Adv. Mater. Res. 591-593:1400–1404. DOI: 10.4028/www.scientific.net/AMR.591-593.1400 View in Article Google Scholar
[141]	Jenie Y.I., van Kampen E.J., de Visser C.C., et al. (2016). Three-dimensional velocity obstacle method for uav’s uncoordinated avoidance maneuver. AIAA Guid. Navig. Control Conf. 2016:1842. DOI:10.2514/6.2016-1842 View in Article Google Scholar
[142]	Emami Y., Zhou H., Almeida L., et al. (2025). Diffusion models for smarter uavs: Decision-making and modeling. arXiv preprint. DOI:10.48550/arXiv.2501.05819 View in Article Google Scholar
[143]	Ali A.M., Gupta A. and Hashim H.A. (2024). Deep reinforcement learning for sim-to-real policy transfer of vtol-uavs offshore docking operations. Appl. Soft Comput.165:111843 DOI: 10.1016/j.asoc.2024.111843 View in Article Google Scholar
[144]	Zhu Y., Chen M., Wang S., et al. (2024). Collaborative reinforcement learning based unmanned aerial vehicle (uav) trajectory design for 3d uav tracking. IEEE Trans. Mob. Comput. 23:10787−10802. DOI:10.1109/TMC.2024.3382913 View in Article CrossRef Google Scholar
[145]	Dash P., Chan E., Lawrence N.P., et al. (2025). Armor: Robust reinforcement learning-based control for uavs under physical attacks. arXiv preprint. DOI:10.48550/arXiv.2506.22423 View in Article Google Scholar
[146]	Tai J.J., Wong J., Innocente M., et al. (2023). Pyflyt–uav simulation environments for reinforcement learning research. arXiv preprint. DOI:10.48550/arXiv.2304.01305 View in Article Google Scholar
[147]	He M., Wang P., Chen H., et al. (2025). Optimization of flying ad hoc network topology and collaborative path planning for multiple uavs. arXiv preprint. DOI:10.48550/arXiv.2506.17945 View in Article Google Scholar
[148]	Xu Z., Han X., Shen H., et al. (2025). Navrl: Learning safe flight in dynamic environments. IEEE Robot. Autom. Lett. DOI:10.1109/LRA.2025.3452541 View in Article Google Scholar
[149]	Xu H., Hu Y., Gao C., et al. (2025). Geonav: Empowering mllms with explicit geospatial reasoning abilities for language-goal aerial navigation. arXiv preprint. DOI:10.48550/arXiv.2504.09587 View in Article Google Scholar
[150]	Wu G., Zhao Z. and He Y. (2023). Relax: Reinforcement learning enabled 2d-lidar autonomous system for parsimonious uavs. arXiv preprint. DOI:10.48550/arXiv.2309.08095 View in Article Google Scholar
[151]	Zhang W., Gao C., Yu S., et al. (2025). Citynavagent: Aerial vision-and-language navigation with hierarchical semantic planning and global memory. Assoc. Comput. Ling. 2025:1511–1521. DOI: 10.18653/v1/2025.acl-long.1511 View in Article Google Scholar
[152]	Zhan Z., Yu L., Yu S., et al. (2024). Mc-gpt: Empowering vision-and-language navigation with memory map and reasoning chains. arXiv preprint. DOI:10.48550/arXiv.2405.10620 View in Article Google Scholar
[153]	Long Y., Cai W., Wang H., et al. (2024). Instructnav: Zero-shot system for generic instruction navigation in unexplored environment. arXiv preprint. DOI:10.48550/arXiv.2406.04882 View in Article Google Scholar
[154]	Shah D., Osiński B., ichter B., et al. (2023). Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action. Conf. Robot Learn. 229:492–504. DOI:10.48550/arXiv.2207.04429 View in Article Google Scholar
[155]	Li D., Chen W. and Lin X. (2024). Tina: Think, interaction, and action framework for zero-shot vision language navigation. arXiv preprint. DOI:10.48550/arXiv.2403.08833 View in Article Google Scholar
[156]	Lin B., Nie Y., Wei Z., et al. (2024). Navcot: Boosting llm-based vision-and-language navigation via learning disentangled reasoning. IEEE Trans. Pattern Anal. Mach. Intell. 46:9423-9438. DOI:10.1109/TPAMI.2024.3457291 View in Article Google Scholar
[157]	Hong Y., Wu Q., Qi Y., et al. (2021). A recurrent vision-and-language bert for navigation. arXiv preprint. DOI:10.48550/arXiv.2011.13922 View in Article Google Scholar
[158]	Zhang X., Tian Y., Zhu F., et al. (2025). Logisticsvln: Vision-language navigation for low-altitude terminal delivery based on agentic uavs. arXiv preprint. DOI:10.48550/arXiv.2505.03460 View in Article Google Scholar
[159]	Cai H., Dong J., Tan J., et al. (2025). Flightgpt: Towards generalizable and interpretable uav vision-and-language navigation with vision-language models. arXiv preprint. DOI:10.48550/arXiv.2505.12835 View in Article Google Scholar
[160]	Gao Y., Wang Z., Jing L., et al. (2024). Exploring spatial representation to enhance llm reasoning in aerial vision-language navigation. arXiv preprint. DOI:10.48550/arXiv.2410.08500 View in Article Google Scholar
[161]	Saxena P., Raghuvanshi N. and Goveas N. (2025). Uav-vln: End-to-end vision language guided navigation for uavs. arXiv preprint. DOI:10.48550/arXiv.2504.21432 View in Article Google Scholar
[162]	Li T., Huai T., Li Z., et al. (2025). Skyvln: Vision-and-language navigation and nmpc control for uavs in urban environments. arXiv preprint. DOI:10.48550/arXiv.2507.06564 View in Article Google Scholar
[163]	Chen J., Lin B., Xu R., et al. (2024). Mapgpt: Map-guided prompting with adaptive path planning for vision-and-language navigation. Assoc. Comput. Ling. 2024.acl-long:9796–9810. DOI: 10.18653/v1/2024.acl-long.529 View in Article Google Scholar
[164]	Long Y., Li X., Cai W., et al. (2024). Discuss before moving: Visual language navigation via multi-expert discussions. IEEE Int. Conf. Robot. Autom. 2024:10807-10813. DOI:10.1109/ICRA57147.2024.10610391 View in Article Google Scholar
[165]	Gao Y., Wang Z., Jing L., et al. (2024). Aerial vision-and-language navigation via semantic-topo-metric representation guided llm reasoning. arXiv preprint. DOI:10.48550/arXiv.2410.08500 View in Article Google Scholar
[166]	Pan R., Xu S. and Brigade S. (2017). Multi-uav cooperative navigation algorithm based on geometric characteristics. J. Ordnance Equip. Eng. 38:55−59. View in Article Google Scholar
[167]	Horyna J., Kratky V., Pritzl V., et al. (2024). Fast swarming of uavs in gnss-denied feature-poor environments without explicit communication. IEEE Robot. Autom. Lett. 9:5284−5291. DOI:10.1109/LRA.2024.3387830 View in Article CrossRef Google Scholar
[168]	Zhang L., Cao X., Su M., et al. (2025). Collaborative integrated navigation for unmanned aerial vehicle swarms under multiple uncertainties. Sensors. 25:617. DOI:10.3390/s25030617 View in Article CrossRef Google Scholar
[169]	Braik M., Al-Hiary H., Alzoubi H., et al. (2025). Tornado optimizer with coriolis force: A novel bio-inspired meta-heuristic algorithm for solving engineering problems. Artif. Intell. Rev. 58:123. DOI:10.1007/s10462-025-11118-9 View in Article CrossRef Google Scholar
[170]	Zhou Z., Zhang Z., Peng X., et al. (2021). A collaborative path planning method for multi-uav based on conflict-based search. Patent. CN113885567A. View in Article Google Scholar
[171]	Tang H., Sun W., Lü L., et al. (2024). Unmanned aerial vehicle formation path planning method based on dynamic reward strategy. Syst. Eng. Electron. 46:3506−3518. DOI:10.12305/j.issn.1001-506X.2024.10.27 View in Article CrossRef Google Scholar
[172]	Zhou Z., Zhang Z., Zhang Z., et al. (2021). A distributed collaborative trajectory planning method for multi-uav targets tracking in complex dynamic environment. Chin. J. Aeronaut. 34:1−13. DOI:10.1016/j.cja.2021.05.001 View in Article CrossRef Google Scholar
[173]	Chen Z., Li S., Khan A.T., et al. (2025). Competition of tribes and cooperation of members algorithm: An evolutionary computation approach for model free optimization. Expert Syst. Appl. 265:125908. DOI:10.1016/j.eswa.2024.125908 View in Article CrossRef Google Scholar
[174]	Han T., Tang A., Zhou H., et al. (2022). Multi-uav cooperative trajectory planning method based on lassa algorithm. Syst. Eng. Electron. 44:233−241. DOI:10.12305/j.issn.1001-506X.2022.01.29 View in Article CrossRef Google Scholar
[175]	Wang Z., Zhang M., Zhang Z., et al. (2024). Uav cooperative path planning based on multi-index dynamic priority. Acta Aeronaut. Astronaut. Sin. 45:328816. DOI:10.7527/S1000-6893.2023.28816 View in Article CrossRef Google Scholar
[176]	Tong P., Yang X., Yang Y., et al. (2023). Multi-uav collaborative absolute vision positioning and navigation: A survey and discussion. Drones. 7:261. DOI:10.3390/drones7040261 View in Article CrossRef Google Scholar
[177]	Leishman R.C., McLain T.W. and Beard R.W. (2014). Relative navigation approach for vision-based aerial gps-denied navigation. J. Intell. Robot. Syst. 74:97−111. DOI:10.1007/s10846-013-9914-7 View in Article CrossRef Google Scholar
[178]	Xu H., Liu P., Chen X., et al. (2024). D²slam: Decentralized and distributed collaborative visual-inertial slam system for aerial swarm. IEEE Trans. Robot. 40:3445−3464. DOI:10.1109/TRO.2024.3411984 View in Article CrossRef Google Scholar
[179]	Wang T., Zhang Y., Liang J., et al. (2020). Multi-uav collaborative system with a feature fast matching algorithm. Front. Inf. Technol. Electron. Eng. 21:1695−1712. DOI:10.1631/FITEE.2000239 View in Article CrossRef Google Scholar
[180]	Huang H., Zhu G., Fan Z., et al. (2022). Vision-based distributed multi-uav collision avoidance via deep reinforcement learning for navigation. IEEE/RSJ Int. Conf. Intell. Robots Syst. 2022:13745–13752. DOI:10.1109/IROS47612.2022.9981803 View in Article Google Scholar
[181]	Mo K., Chu L., Zhang X., et al. (2024). Dral: Deep reinforcement adaptive learning for multi-uavs navigation in unknown indoor environment. IEEE Int. Conf. Mechatron. Control Eng. 2024:1–6. DOI:10.1109/MCTE62870.2024.00012 View in Article Google Scholar
[182]	Xu Y., Wei Y., Jiang K., et al. (2023). Multiple uavs path planning based on deep reinforcement learning in communication denial environment. Mathematics. 11:405. DOI:10.3390/math11020405 View in Article CrossRef Google Scholar
[183]	Wang J., Yu Z., Zhou D., et al. (2024). Vision-based deep reinforcement learning of uav autonomous navigation using privileged information. arXiv preprint. DOI:10.48550/arXiv.2412.06313 View in Article Google Scholar
[184]	Duffy J.P., Cunliffe A.M., DeBell L., et al. (2018). Location, location, location: Considerations when using lightweight drones in challenging environments. Remote Sens. Ecol. Conserv. 4:7−19. DOI:10.1002/rse2.89 View in Article CrossRef Google Scholar
[185]	Zhu P., Wen L., Bian X., et al. (2018). Vision meets drones: A challenge. arXiv preprint. DOI:10.48550/arXiv.1804.07437 View in Article Google Scholar
[186]	Couturier A. and Akhloufi M.A. (2021). A review on absolute visual localization for uav. Robot. Auton. Syst. 135:103666. DOI:10.1016/j.robot.2020.103666 View in Article CrossRef Google Scholar
[187]	Russell J.S., Ye M., Anderson B.D.O., et al. (2020). Cooperative localization of a gps-denied uav using direction-of-arrival measurements. IEEE Trans. Aerosp. Electron. Syst. 56:1966−1978. DOI:10.1109/TAES.2019.2950136 View in Article CrossRef Google Scholar
[188]	Hou Y., Zhang H. and Zhou S. (2015). Convolutional neural network-based image representation for visual loop closure detection. IEEE Int. Conf. Inf. Autom. 2015:2238–2245. DOI:10.1109/ICInfA.2015.7279665 View in Article Google Scholar
[189]	Al-Jarrah O.Y., Shatnawi A.S., Shurman M.M., et al. (2024). Exploring deep learning-based visual localization techniques for uavs in gps-denied environments. IEEE Access. 12:113049−113071. DOI:10.1109/ACCESS.2024.3439350 View in Article CrossRef Google Scholar
[190]	Sautenkov O., Yaqoot Y., Lykov A., et al. (2025). Uav-vla: Vision-language-action system for large scale aerial mission generation. arXiv preprint. DOI:10.48550/arXiv.2501.05014 View in Article Google Scholar
[191]	Ding X., Zhang X., Ma N., et al. (2021). Repvgg: Making vgg-style convnets great again. IEEE/CVF Conf. Comput. Vis. Pattern Recogn. 2021:13733–13742. DOI:10.1109/CVPR46437.2021.01352 View in Article Google Scholar
[192]	Shuvo M.M.H., Islam S.K., Cheng J., et al. (2023). Efficient acceleration of deep learning inference on resource-constrained edge devices: A review. Proc. IEEE. 111:42−91. DOI:10.1109/JPROC.2022.3226530 View in Article CrossRef Google Scholar
[193]	Huang X. (2025). The small-drone revolution is coming — scientists need to ensure it will be safe. Nature. 637:29−30. DOI:10.1038/d41586-024-04167-7 View in Article CrossRef Google Scholar
[194]	Jin W., Yang J., Fang Y., et al. (2020). Research on application and deployment of uav in emergency response. IEEE Int. Conf. Electron. Inf. Emerg. Commun. 2020:277–280. DOI:10.1109/ICEIEC49280.2020.9152338 View in Article Google Scholar
[195]	Wolfe V., Frobe W., Shrinivasan V., et al. (2015). Detecting and locating cell phone signals from avalanche victims using unmanned aerial vehicles. Int. Conf. Unmanned Aircr. Syst. 2015:704–713. DOI:10.1109/ICUAS.2015.7152352 View in Article Google Scholar
[196]	Bejiga M.B., Zeggada A. and Melgani F. (2016). Convolutional neural networks for near real-time object detection from uav imagery in avalanche search and rescue operations. IEEE Int. Geosci. Remote Sens. Symp. 2016:693–696. DOI:10.1109/IGARSS.2016.7729163 View in Article Google Scholar
[197]	Martinez-Alpiste I., Golcarenarenji G., Wang Q., et al. (2021). Search and rescue operation using uavs: A case study. Expert Syst. Appl. 178:114937. DOI:10.1016/j.eswa.2021.114937 View in Article CrossRef Google Scholar
[198]	Jo D. and Kwon Y. (2017). Development of rescue material transport uav (unmanned aerial vehicle). World J. Eng. Technol. 5:720−729. DOI:10.4236/wjet.2017.54061 View in Article CrossRef Google Scholar
[199]	Zhang X., Zhao H., Zhang J., et al. (2023). Cooperative trajectory design of multiple uav base stations with heterogeneous graph neural networks. IEEE Trans. Wireless Commun. 22:1495−1509. DOI:10.1109/TWC.2022.3204794 View in Article CrossRef Google Scholar
[200]	Yuan X., Zhang J., Zhao J., et al. (2024). Poster abstract: Emergency networking using uavs: A reinforcement learning approach with large language model. Int. Conf. Inf. Process. Sensor Netw. 2024:287–288. DOI:10.1109/IPSN61024.2024.00056 View in Article Google Scholar
[201]	Yong S.P. and Yeong Y.C. (2018). Human object detection in forest with deep learning based on drone’s vision. Int. Conf. Comput. Inf. Sci. 2018:1–5. DOI:10.1109/ICCOINS.2018.8510564 View in Article Google Scholar
[202]	Menouar H., Guvenc I., Akkaya K., et al. (2017). Uav-enabled intelligent transportation systems for the smart city: Applications and challenges. IEEE Commun. Mag. 55:22−28. DOI:10.1109/MCOM.2017.1600238CM View in Article CrossRef Google Scholar
[203]	Telikani A., Sarkar A., Du B., et al. (2024). Machine learning for uav-aided its: A review with comparative study. IEEE Trans. Intell. Transp. Syst. 25:15388−15406. DOI:10.1109/TITS.2024.3422039 View in Article CrossRef Google Scholar
[204]	Du Q., Dong W., Su W., et al. (2022). Uav inspection technology and application of transmission line. IEEE Int. Conf. Inf. Syst. Comput. Aided Educ. 2022:594–597. DOI:10.1109/ICISCAE57069.2022.10075457 View in Article Google Scholar
[205]	Duan H., Shi F., Gao B., et al. (2025). A novel real-time intelligent detector for monitoring uavs in live-line operation on 10 kv distribution networks. Intell. Robot. 5:70−87. DOI:10.20517/ir.2025.05 View in Article CrossRef Google Scholar
[206]	Ince E. (2024). Transmission line inspection with uavs: A review of technologies, applications, and challenges. Int. J. Energy Smart Grid. 9:60−69. DOI:10.23884/ijesg.2024.9.1.05 View in Article CrossRef Google Scholar
[207]	Iversen N., Schofield O.B. and Ebeid E. (2020). Locator - lightweight and low-cost autonomous drone system for overhead cable detection and soft grasping. IEEE Int. Symp. Saf. Secur. Rescue Robot. 2020:205–212. DOI:10.1109/SSRR50563.2020.9292591 View in Article Google Scholar
[208]	Dadrass Javan F., Samadzadegan F., Toosi A., et al. (2025). Unmanned aerial geophysical remote sensing: A systematic review. Remote Sens. 17:110. DOI:10.3390/rs17010110 View in Article CrossRef Google Scholar
[209]	Iglay R.B., Jones L.R., Elmore J.E., et al. (2024). Wildlife monitoring with drones: A survey of end users. Wildl. Soc. Bull. 48:e1533. DOI:10.1002/wsb.1533 View in Article CrossRef Google Scholar
[210]	Su J., Zhu X., Li S., et al. (2023). Ai meets uavs: A survey on ai empowered uav perception systems for precision agriculture. Neurocomputing. 518:242−270. DOI:10.1016/j.neucom.2022.10.072 View in Article CrossRef Google Scholar
[211]	Kerkech M., Hafiane A. and Canals R. (2020). Vine disease detection in uav multispectral images with deep learning segmentation approach. Comput. Electron. Agric. 169:105236. DOI:10.1016/j.compag.2019.105236 View in Article CrossRef Google Scholar
[212]	Rajagopal M.K. and Sarker B.M. (2023). Artificial intelligence based drone for early disease detection and precision pesticide management in cashew farming. arXiv preprint. DOI:10.48550/arXiv.2303.08556. View in Article Google Scholar
[213]	Zhou Y., Lao C., Yang Y., et al. (2021). Diagnosis of winter-wheat water stress based on uav-borne multispectral image texture and vegetation indices. Agric. Water Manag. 256:107076. DOI:10.1016/j.agwat.2021.107076 View in Article CrossRef Google Scholar
[214]	Huang Y., Hoffmann W.C., Lan Y., et al. (2015). Development of a low-volume sprayer for an unmanned helicopter. J. Agric. Sci. 7:148−153. DOI:10.5539/jas.v7n1p148 View in Article CrossRef Google Scholar
[215]	Shendryk Y., Sofonia J., Skocaj G., et al. (2020). Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using uav lidar and multispectral imaging. Int. J. Appl. Earth Obs. Geoinf. 92:102177. DOI:10.1016/j.jag.2020.102177 View in Article CrossRef Google Scholar
[216]	Huang X. (2025). The small-drone revolution is coming — scientists need to ensure it will be safe. Nature. 637:29−30. DOI:10.1038/d41586-024-04167-7 View in Article CrossRef Google Scholar
[217]	Wang L., Wang L., Qiao Z., et al. (2022). The optimization of the "uav-vehicle" joint delivery route considering mountainous cities. PLoS One. 17:e0265518. DOI:10.1371/journal.pone.0265518 View in Article CrossRef Google Scholar
[218]	Li X., Tupayachi J., Sharmin A., et al. (2023). Drone-aided delivery methods, challenge, and the future: A methodological review. Drones. 7:191. DOI:10.3390/drones7030191 View in Article CrossRef Google Scholar
[219]	Gaia Consulting Oy. (2021). Potential benefits of drone deliveries in Helsinki. https://kstatic.googleusercontent.com/files/cccdb99966ab71d08af6b990e4e3ff687751122130934f82f55f960ecfcf75a987ec36a5ae4be130c482824eeb0febc6790d8346a3a707508165c045c2c74436 View in Article Google Scholar
[220]	Awasthi S., Gramse N., Reining C., et al. (2022). Uavs for industries and supply chain management. arXiv preprint. DOI:10.48550/arXiv.2212.03346 View in Article Google Scholar
[221]	Rejeb A., Rejeb K., Simske S., et al. (2023). Drones for supply chain management and logistics: A review and research agenda. Int. J. Logist. Res. Appl. 26:708−731. DOI:10.1080/13675567.2021.1981273 View in Article CrossRef Google Scholar
[222]	Das A., Datta S., Gkioxari G., et al. (2018). Embodied question answering. IEEE Conf. Comput. Vis. Pattern Recogn. 2018:1–10. DOI:10.1109/CVPR.2018.00006 View in Article Google Scholar
[223]	Mu Y., Zhang Q., Hu M., et al. (2023). Embodiedgpt: Vision-language pre-training via embodied chain of thought. arXiv preprint. DOI:10.48550/arXiv.2305.15021 View in Article Google Scholar
[224]	Shah D., Osiński B., Ichter B., et al. (2022). Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action. arXiv preprint. DOI:10.48550/arXiv.2207.04429 View in Article Google Scholar
[225]	Wu Z., Wang Z., Xu X., et al. (2023). Embodied task planning with large language models. arXiv preprint. DOI:10.48550/arXiv.2307.01848 View in Article Google Scholar
[226]	Tian H., Meng J., Zheng W.-S., et al. (2024). Loc4plan: Locating before planning for outdoor vision and language navigation. arXiv preprint. DOI:10.48550/arXiv.2408.05090 View in Article Google Scholar
[227]	Chen J., Lin B., Xu R., et al. (2024). Mapgpt: Map-guided prompting with adaptive path planning for vision-and-language navigation. arXiv preprint. DOI:10.48550/arXiv.2401.07314 View in Article Google Scholar
[228]	Hong Y., Zhen H., Chen P., et al. (2023). 3d-llm: Injecting the 3d world into large language models. Neural Inf. Process. Syst. DOI:10.48550/arXiv.2307.12981 View in Article Google Scholar
[229]	Luo J., Liu Y., Chen W., et al. (2025). Dspnet: Dual-vision scene perception for robust 3d question answering. arXiv preprint. DOI:10.48550/arXiv.2503.03190 View in Article Google Scholar
[230]	Saxena P., Raghuvanshi N. and Goveas N. (2025). Uav-vln: End-to-end vision language guided navigation for uavs. arXiv preprint. DOI:10.48550/arXiv.2504.21432 View in Article Google Scholar
[231]	Wang X., Yang D., Wang Z., et al. (2024). Towards realistic uav vision-language navigation: Platform, benchmark, and methodology. arXiv preprint. DOI:10.48550/arXiv.2410.07087 View in Article Google Scholar
[232]	Messaoudi F., Simon G. and Ksentini A. (2015). Dissecting games engines: The case of unity3d. Int. Workshop Netw. Syst. Support Games. 2015:1–6. DOI:10.1109/NetGames.2015.7382997 View in Article Google Scholar
[233]	Shah S., Dey D., Lovett C., et al. (2018). Airsim: High-fidelity visual and physical simulation for autonomous vehicles. Field Serv. Robot. 2018:621–635. DOI:10.1007/978-3-319-67361-5_40 View in Article Google Scholar
[234]	Unreal Engine. (2018). Unreal engine. https://www.unrealengine.com View in Article Google Scholar
[235]	Yao Y., Sun C., Wang T., et al. (2024). Uav geo-localization dataset and method based on cross-view matching. Sensors. 24:6905. DOI:10.3390/s24216905 View in Article CrossRef Google Scholar
[236]	Sun Z., Liu Y., Zhu H., et al. (2025). Refdrone: A challenging benchmark for referring expression comprehension in drone scenes. arXiv preprint. DOI:10.48550/arXiv.2502.00392 View in Article Google Scholar
[237]	Xiao J., Sun Y., Shao Y., et al. (2025). Uav-on: A benchmark for open-world object goal navigation with aerial agents. arXiv preprint. DOI:10.48550/arXiv.2508.00288 View in Article Google Scholar
[238]	Wang S., Zhou D., Xie L., et al. (2025). Panogen++: Domain-adapted text-guided panoramic environment generation for vision-and-language navigation. Neural Netw. 187:107320. DOI:10.1016/j.neunet.2025.107320 View in Article CrossRef Google Scholar
[239]	Meegahapola L., Hassoune H. and Gatica-Perez D. (2024). M3bat: Unsupervised domain adaptation for multimodal mobile sensing with multi-branch adversarial training. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 8:1−30. DOI:10.1145/3659623 View in Article CrossRef Google Scholar
[240]	Mumuni A., Mumuni F. and Gerrar N.K. (2024). A survey of synthetic data augmentation methods in machine vision. Mach. Intell. Res. 21:831−869. DOI:10.1007/s11633-022-1411-7 View in Article CrossRef Google Scholar
[241]	Vettoruzzo A., Bouguelia M.-R., Vanschoren J., et al. (2024). Advances and challenges in meta-learning: A technical review. IEEE Trans. Pattern Anal. Mach. Intell. 46:4763−4779. DOI:10.1109/TPAMI.2024.3357847 View in Article CrossRef Google Scholar
[242]	Xu X., Li M., Tao C., et al. (2024). A survey on knowledge distillation of large language models. arXiv preprint. DOI:10.48550/arXiv.2402.13116 View in Article Google Scholar
[243]	Long X., Zhao Q., Zhang K., et al. (2025). A survey: Learning embodied intelligence from physical simulators and world models. arXiv preprint. DOI:10.48550/arXiv.2507.00917 View in Article Google Scholar

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Yao F., Liu Y., Zhang W., et al. (2025). AeroVerse-Review: Comprehensive survey on aerial embodied vision-and-language navigation. The Innovation Informatics 1:100015. https://doi.org/10.59717/j.xinn-inform.2025.100015

Yao F., Liu Y., Zhang W., et al. (2025). AeroVerse-Review: Comprehensive survey on aerial embodied vision-and-language navigation. The Innovation Informatics 1:100015. https://doi.org/10.59717/j.xinn-inform.2025.100015

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