Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial

TL;DR

This paper surveys how Large Language Models (LLMs) can enhance UAV operations, covering architectures, applications, and ethical considerations, and introduces a unified framework for integrating LLMs into UAV systems.

Contribution

It provides a comprehensive taxonomy and framework for applying LLMs to UAVs, including methodologies, applications, and ethical aspects, advancing the understanding of intelligent aerial systems.

Findings

Systematic taxonomy of LLM adaptation techniques for UAVs

Analysis of LLM-assisted UAV applications in navigation and swarm control

Discussion of ethical issues and future research directions in LLM-UAV integration

Abstract

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.