Recent Advances in Transformer and Large Language Models for UAV Applications

2025-08-15

AI Summary

This survey addresses the lack of a unified framework for Transformer applications in unmanned aerial vehicle (UAV) systems. We systematically review state-of-the-art Transformer-based approaches across three core tasks: perception, decision-making, and autonomous control. Methodologically, we propose— for the first time—a four-dimensional taxonomy encompassing attention mechanisms, CNN-Transformer hybrids, reinforcement learning–enabled Transformers, and large language model (LLM)-integrated architectures. Leveraging standardized datasets, simulation platforms, and evaluation metrics, we conduct structured comparative analysis and performance benchmarking, identifying critical bottlenecks including low computational efficiency and challenges in real-time deployment. Our analysis further uncovers technical gaps in emerging application domains such as precision agriculture and autonomous navigation. Key future directions are identified: LLM–reinforcement learning fusion, model lightweighting, and edge-device协同 optimization. The work delivers a reproducible technical roadmap and practical guidelines for advancing Transformer-enabled UAV intelligence.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Multiagent Systems: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

The rapid advancement of Transformer-based models has reshaped the landscape of uncrewed aerial vehicle (UAV) systems by enhancing perception, decision-making, and autonomy. This review paper systematically categorizes and evaluates recent developments in Transformer architectures applied to UAVs, including attention mechanisms, CNN-Transformer hybrids, reinforcement learning Transformers, and large language models (LLMs). Unlike previous surveys, this work presents a unified taxonomy of Transformer-based UAV models, highlights emerging applications such as precision agriculture and autonomous navigation, and provides comparative analyses through structured tables and performance benchmarks. The paper also reviews key datasets, simulators, and evaluation metrics used in the field. Furthermore, it identifies existing gaps in the literature, outlines critical challenges in computational efficiency and real-time deployment, and offers future research directions. This comprehensive synthesis aims to guide researchers and practitioners in understanding and advancing Transformer-driven UAV technologies.

Problem

• Reviewing Transformer architectures for UAV perception and autonomy
• Providing taxonomy and benchmarks for UAV-specific Transformer models
• Identifying computational efficiency challenges in real-time UAV deployment

Innovation

• Transformer architectures for UAV perception and autonomy
• CNN-Transformer hybrid models with attention mechanisms
• LLM-enhanced reinforcement learning for autonomous navigation