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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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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