Large Language Models for UAVs: Current State and Pathways to the Future

Large Language Models for UAVs: Current State and Pathways to the Future

2 May 2024 | Shumaila Javaid Member, IEEE, Nasir Saeed Senior Member, IEEE, Bin He Senior Member, IEEE
The paper "Large Language Models for UAVs: Current State and Pathways to the Future" by Shumaila Javaid, Nasir Saeed, and Bin He explores the integration of Large Language Models (LLMs) into Unmanned Aerial Vehicles (UAVs) to enhance their capabilities. The authors review various LLM architectures, evaluate their suitability for UAV integration, and discuss state-of-the-art LLM-based UAV architectures. They highlight the potential of LLMs in refining data analysis and decision-making processes, particularly in spectral sensing and sharing. The paper also examines how LLM integration expands the scope of UAV applications, enabling autonomous data processing, improved decision-making, and faster response times in emergency scenarios. Additionally, the authors identify critical areas for future research to facilitate the effective integration of LLMs and UAVs, addressing legal, ethical, and technical challenges. The paper concludes by summarizing the findings and reflecting on the broader implications of integrating LLMs into UAV systems.The paper "Large Language Models for UAVs: Current State and Pathways to the Future" by Shumaila Javaid, Nasir Saeed, and Bin He explores the integration of Large Language Models (LLMs) into Unmanned Aerial Vehicles (UAVs) to enhance their capabilities. The authors review various LLM architectures, evaluate their suitability for UAV integration, and discuss state-of-the-art LLM-based UAV architectures. They highlight the potential of LLMs in refining data analysis and decision-making processes, particularly in spectral sensing and sharing. The paper also examines how LLM integration expands the scope of UAV applications, enabling autonomous data processing, improved decision-making, and faster response times in emergency scenarios. Additionally, the authors identify critical areas for future research to facilitate the effective integration of LLMs and UAVs, addressing legal, ethical, and technical challenges. The paper concludes by summarizing the findings and reflecting on the broader implications of integrating LLMs into UAV systems.
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