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[2602.19534] Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial
Summary
This article surveys the integration of Large Language Models (LLMs) in Uncrewed Aerial Vehicles (UAVs), exploring their potential to enhance UAV operations through advanced environmental understanding and swarm coordination.
Why It Matters
The integration of LLMs into UAV systems represents a significant advancement in robotics and AI, enabling more intelligent and adaptive aerial operations. This research highlights the potential for improved navigation, mission planning, and safety, which are crucial for the future of UAV applications across various industries.
Key Takeaways
- LLMs can enhance UAV intelligence beyond traditional methods.
- A structured taxonomy of LLM adaptation techniques for UAVs is proposed.
- The paper discusses ethical considerations in LLM-assisted UAV operations.
- Future research directions are outlined to guide advancements in this field.
- Integration of LLMs can improve swarm coordination and environmental understanding.
Computer Science > Robotics arXiv:2602.19534 (cs) [Submitted on 23 Feb 2026] 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 View a PDF of the paper titled Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial, by Yousef Emami and 7 other authors View PDF HTML (experimental) 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 Learn...
