大型语言模型辅助的无人机操作与通信：多方面的综述与教程
Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial
AI总结：
本文综述了大型语言模型在无人机操作与通信中的应用，探讨了LLMs在提升UAV智能方面的多方面技术与未来研究方向。
摘要：
无人驾驶航空器（UAVs）因其机动性和敏捷性而被广泛应用于各种应用。近年来，大型语言模型（LLMs）的进步为提升UAV智能提供了变革性的机会。通过将LLMs集成到UAV系统中，可以实现先进的环境理解、群体协调、移动优化和高层任务推理，从而实现更适应性和情境感知的空中操作。本文系统地探讨了LLMs与UAV技术的交叉领域，并提出了一种统一的框架，整合了现有的架构、方法和应用。我们首先呈现了LLMs在UAV上的适应技术的结构化分类，包括预训练、微调、检索增强生成（RAG）和提示工程，以及关键推理能力如链式思考（CoT）和上下文学习（ICL）。然后，我们研究了LLMs辅助的UAV通信和操作，涵盖导航、任务规划、群体控制、安全、自主性和网络管理。之后，本文进一步讨论了多模态LLMs（MLLMs）在人-群体交互、感知驱动导航和协作控制中的应用。最后，我们讨论了伦理问题，包括偏见、透明度、责任和人机协作（HITL）策略，并概述了未来的研究方向。总体而言，本文将LLMs辅助的UAVs定位为智能和适应性空中系统的基础。
英文摘要：
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.
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。
