Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology across diverse sectors, offering adaptable solutions to complex challenges in both military and civilian domains. Their expanding capabilities present a platform for further advancement by integrating cutting-edge computational tools like Artificial Intelligence (AI) and Machine Learning (ML) algorithms. These advancements have significantly impacted various facets of human life, fostering an era of unparalleled efficiency and convenience. Large Language Models (LLMs), a key component of AI, exhibit remarkable learning and adaptation capabilities within deployed environments, demonstrating an evolving form of intelligence with the potential to approach human-level proficiency. This work explores the significant potential of integrating UAVs and LLMs to propel the development of autonomous systems. We comprehensively review LLM architectures, evaluating their suitability for UAV integration. Additionally, we summarize the state-of-the-art LLM-based UAV architectures and identify novel opportunities for LLM embedding within UAV frameworks. Notably, we focus on leveraging LLMs to refine data analysis and decision-making processes, specifically for enhanced spectral sensing and sharing in UAV applications. Furthermore, we investigate how LLM integration expands the scope of existing UAV applications, enabling autonomous data processing, improved decision-making, and faster response times in emergency scenarios like disaster response and network restoration. Finally, we highlight crucial areas for future research that are critical for facilitating the effective integration of LLMs and UAVs.

翻译：无人机（UAV）已成为跨多领域的变革性技术，为军事和民用领域的复杂挑战提供适应性解决方案。其日益增强的能力为整合人工智能（AI）与机器学习（ML）算法等尖端计算工具提供了进一步发展的平台。这些进步深刻影响了人类生活的诸多方面，开启了前所未有的高效与便捷时代。作为人工智能的关键组成部分，大语言模型（LLM）在部署环境中展现出卓越的学习与适应能力，呈现出一种可能接近人类水平的持续进化型智能。本文探讨了整合无人机与大语言模型以推动自主系统发展的巨大潜力。我们全面梳理了大语言模型架构，评估其与无人机集成的适用性。此外，我们总结了基于大语言模型的最先进无人机架构，并识别了将大语言模型嵌入无人机框架的新机遇。特别地，我们聚焦于利用大语言模型优化数据分析与决策过程，具体针对无人机应用中的增强型频谱感知与共享。进一步地，我们研究了大语言模型集成如何拓展现有无人机应用范围，实现自主数据处理、优化决策制定，并在灾害响应、网络恢复等紧急场景中缩短响应时间。最后，我们指出了未来研究的关键方向，这些方向对于促进大语言模型与无人机的有效集成至关重要。