Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908965984534528 |
|---|---|
| author | Chen, Hanxuan Zheng, Jie Yang, Siqi Zeng, Tianle Feng, Siwei Cheng, Songsheng Ren, Ruilong Guo, Hanzhong Yuan, Shuai Wang, Xiangyue Wang, Kangli Pei, Ji |
| author_facet | Chen, Hanxuan Zheng, Jie Yang, Siqi Zeng, Tianle Feng, Siwei Cheng, Songsheng Ren, Ruilong Guo, Hanzhong Yuan, Shuai Wang, Xiangyue Wang, Kangli Pei, Ji |
| contents | Vision-and-Language Navigation for Unmanned Aerial Vehicles (UAV-VLN) represents a pivotal challenge in embodied artificial intelligence, focused on enabling UAVs to interpret high-level human commands and execute long-horizon tasks in complex 3D environments. This paper provides a comprehensive and structured survey of the field, from its formal task definition to the current state of the art. We establish a methodological taxonomy that charts the technological evolution from early modular and deep learning approaches to contemporary agentic systems driven by large foundation models, including Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, and the emerging integration of generative world models with VLA architectures for physically-grounded reasoning. The survey systematically reviews the ecosystem of essential resources simulators, datasets, and evaluation metrics that facilitates standardized research. Furthermore, we conduct a critical analysis of the primary challenges impeding real-world deployment: the simulation-to-reality gap, robust perception in dynamic outdoor settings, reasoning with linguistic ambiguity, and the efficient deployment of large models on resource-constrained hardware. By synthesizing current benchmarks and limitations, this survey concludes by proposing a forward-looking research roadmap to guide future inquiry into key frontiers such as multi-agent swarm coordination and air-ground collaborative robotics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13654 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap Chen, Hanxuan Zheng, Jie Yang, Siqi Zeng, Tianle Feng, Siwei Cheng, Songsheng Ren, Ruilong Guo, Hanzhong Yuan, Shuai Wang, Xiangyue Wang, Kangli Pei, Ji Robotics Vision-and-Language Navigation for Unmanned Aerial Vehicles (UAV-VLN) represents a pivotal challenge in embodied artificial intelligence, focused on enabling UAVs to interpret high-level human commands and execute long-horizon tasks in complex 3D environments. This paper provides a comprehensive and structured survey of the field, from its formal task definition to the current state of the art. We establish a methodological taxonomy that charts the technological evolution from early modular and deep learning approaches to contemporary agentic systems driven by large foundation models, including Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, and the emerging integration of generative world models with VLA architectures for physically-grounded reasoning. The survey systematically reviews the ecosystem of essential resources simulators, datasets, and evaluation metrics that facilitates standardized research. Furthermore, we conduct a critical analysis of the primary challenges impeding real-world deployment: the simulation-to-reality gap, robust perception in dynamic outdoor settings, reasoning with linguistic ambiguity, and the efficient deployment of large models on resource-constrained hardware. By synthesizing current benchmarks and limitations, this survey concludes by proposing a forward-looking research roadmap to guide future inquiry into key frontiers such as multi-agent swarm coordination and air-ground collaborative robotics. |
| title | Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.13654 |