arXiv 2604.136542026-04-15cs.RO

面向无人机的视觉与语言导航：进展、挑战与研究路线图

Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap

Autel Robotics, Shenzhen, China（大疆创新，深圳，中国）；School of Intelligent Software and Engineering, Nanjing University, Nanjing, China（南京大学智能软件与工程学院，南京，中国）；School of Computer, Data & Information Sciences, University of Wisconsin-Madison, Madison, WI, USA（威斯康星大学麦迪逊分校计算机、数据与信息科学学院，麦迪逊，WI，美国）；Southern University of Science and Technology, Shenzhen, China（南方科技大学，深圳，中国）；The University of Hong Kong, Hong Kong SAR, China（香港大学，香港特别行政区，中国）；School of Software and Microelectronics, Peking University, Beijing, China（北京大学软件与微电子学院，北京，中国）

Hanxuan Chen, Jie Zheng, Siqi Yang, Tianle Zeng, Siwei Feng, Songsheng Cheng, Ruilong Ren, Hanzhong Guo, Shuai Yuan, Xiangyue Wang, Kangli Wang, Ji Pei

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AI总结：

本文综述了无人机视觉与语言导航领域，分析了从早期模块化方法到基于大模型的智能系统的发展，探讨了现实部署中的挑战，并提出了未来研究方向。

摘要：

面向无人机的视觉与语言导航（UAV-VLN）代表了嵌入式人工智能中的关键挑战，旨在使无人机在复杂3D环境中解读高层人类指令并执行长周期任务。本文提供了该领域的全面系统性综述，从正式任务定义到当前最先进方法。我们建立了方法论分类学，追踪了从早期模块化和深度学习方法到当前基于大基础模型的智能系统的技术演变，包括视觉-语言模型（VLMs）、视觉-语言-动作（VLA）模型以及新兴的生成世界模型与VLA架构结合的物理基础推理。该综述系统地回顾了促进标准化研究的必要资源、模拟器、数据集和评估指标生态系统。此外，我们还对阻碍现实部署的主要挑战进行了批判性分析：仿真到现实的差距、动态户外环境中的鲁棒感知、语言歧义的推理以及在资源受限硬件上高效部署大模型。通过综合当前基准和限制，本文最后提出了一项前瞻性研究路线图，以指导未来对多智能体群体协调和空地协作机器人等关键前沿领域的研究。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。