Uncrewed Aerial Vehicles (UAVs) are widely deployed across diverse applications due to their mobility and agility. Recent advances in Large Language Models (LLMs) offer a transformative opportunity to enhance UAV intelligence beyond conventional optimization-based and learning-based approaches. By integrating LLMs into UAV systems, advanced environmental understanding, swarm coordination, mobility optimization, and high-level task reasoning can be achieved, thereby allowing more adaptive and context-aware aerial operations. This survey systematically explores the intersection of LLMs and UAV technologies and proposes a unified framework that consolidates existing architectures, methodologies, and applications for UAVs. We first present a structured taxonomy of LLM adaptation techniques for UAVs, including pretraining, fine-tuning, Retrieval-Augmented Generation (RAG), and prompt engineering, along with key reasoning capabilities such as Chain-of-Thought (CoT) and In-Context Learning (ICL). We then examine LLM-assisted UAV communications and operations, covering navigation, mission planning, swarm control, safety, autonomy, and network management. After that, the survey further discusses Multimodal LLMs (MLLMs) for human-swarm interaction, perception-driven navigation, and collaborative control. Finally, we address ethical considerations, including bias, transparency, accountability, and Human-in-the-Loop (HITL) strategies, and outline future research directions. Overall, this work positions LLM-assisted UAVs as a foundation for intelligent and adaptive aerial systems.

翻译：无人机凭借其机动性与灵活性，已在众多应用领域得到广泛部署。大型语言模型的最新进展为提升无人机智能提供了超越传统基于优化与基于学习方法的变革性机遇。通过将LLM集成至无人机系统，可实现高级环境理解、集群协同、移动性优化与高层任务推理，从而实现更具适应性与情境感知的空中操作。本综述系统性地探讨了LLM与无人机技术的交叉领域，并提出一个统一框架以整合现有无人机架构、方法与应用。我们首先提出面向无人机的LLM适配技术结构化分类体系，包括预训练、微调、检索增强生成与提示工程，以及关键推理能力如思维链与上下文学习。随后，我们审视LLM辅助的无人机通信与操作，涵盖导航、任务规划、集群控制、安全性、自主性与网络管理。进而，本综述深入讨论了多模态LLM在人-群交互、感知驱动导航与协同控制中的应用。最后，我们探讨了伦理考量，包括偏见、透明度、问责制及人在回路策略，并展望了未来研究方向。总体而言，本工作将LLM辅助的无人机定位为构建智能自适应空中系统的基础。