Recent Advances in Transformer and Large Language Models for UAV Applications
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908491593023488 |
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| author | Kheddar, Hamza Habchi, Yassine Ghanem, Mohamed Chahine Hemis, Mustapha Niyato, Dusit |
| author_facet | Kheddar, Hamza Habchi, Yassine Ghanem, Mohamed Chahine Hemis, Mustapha Niyato, Dusit |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11834 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recent Advances in Transformer and Large Language Models for UAV Applications Kheddar, Hamza Habchi, Yassine Ghanem, Mohamed Chahine Hemis, Mustapha Niyato, Dusit Computer Vision and Pattern Recognition Artificial Intelligence Robotics Systems and Control Image and Video Processing 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. |
| title | Recent Advances in Transformer and Large Language Models for UAV Applications |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Robotics Systems and Control Image and Video Processing |
| url | https://arxiv.org/abs/2508.11834 |