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

• URL: http://arxiv.org/abs/2602.19534v2
• Date: Sun, 01 Mar 2026 06:58:50 GMT
• Title: Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial
• Authors: Yousef Emami, Hao Zhou, Radha Reddy, Atefeh Hajijamali Arani, Biliang Wang, Kai Li, Luis Almeida, Zhu Han,
• Abstract summary: Uncrewed Aerial Vehicles (UAVs) are widely deployed across diverse applications due to their mobility and agility.<n>Recent advances in Large Language Models (LLMs) offer a transformative opportunity to enhance UAV intelligence.
• Score: 24.92730472637731
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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