系统梳理了Transformer在无人机中的应用进展
Recent Advances in Transformer and Large Language Models for UAV Applications
- 构建统一分类框架，涵盖注意力机制与混合模型
- 对比分析多种架构在农业与导航任务中的表现
- 适合关注无人机智能升级的研究者与工程师
基于Transformer的模型快速发展，显著提升了无人飞行器（UAV）的感知、决策与自主能力。本文系统分类并评估了应用于无人机的Transformer架构进展，包括注意力机制、CNN-Transformer混合模型、强化学习-Transformer以及大语言模型（LLMs）。相较于以往综述，本工作提出统一的分类体系，突出精准农业与自主导航等新兴应用，并通过结构化表格和性能基准进行对比分析。论文还梳理了该领域关键数据集、仿真平台与评估指标，识别出计算效率与实时部署方面的现存挑战，并提出未来研究方向。本综述旨在为研究人员与实践者提供全面参考，推动基于Transformer的无人机技术发展。
原文摘要 · Abstract (English)
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.
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