In recent years, unmanned aerial vehicles (UAVs) have become increasingly popular in our daily lives and have attracted significant research interest in software engineering. At the same time, large language models (LLMs) have made notable advancements in language understanding, reasoning, and generation, making LLM applications in UAVs a promising research direction. However, existing studies have largely remained in preliminary exploration with a limited understanding of real-world practice, which causes an academia-industry gap and hinders the application of LLMs in UAVs. To address this, we conducted the first empirical study to investigate how LLMs support UAVs. To characterize common tasks and application scenarios of real-world UAV-LLM practices, we conducted a large-scale empirical study involving 997 research papers and 1,509 GitHub projects. The results classified nine common tasks (e.g., Natural Language Command Parsing) in four UAV workflows (e.g., Information Input) undertaken by LLMs in real-world UAV projects and revealed a large difference in the task distribution of research efforts and industry practices. To gain deeper insight into these differences and understand developers' perspectives on the application of LLMs in UAVs, we conducted a survey of practitioners, receiving 52 valid responses from 15 countries. The results revealed that while 40.4% of developers have attempted to apply LLMs to UAV tasks, 59.6% still face challenges integrating their UAV projects with advanced LLM capabilities. Their feedback attributes these challenges to five factors, including technological maturity, performance, safety, cost, and others, and provides practical implications for researchers and developers in conducting UAV-LLM practices.

翻译：近年来，无人机在人们日常生活中日益普及，并引发了软件工程领域的广泛研究兴趣。与此同时，大语言模型在语言理解、推理和生成方面取得了显著进展，使得大语言模型在无人机中的应用成为一个有前景的研究方向。然而，现有研究大多停留在初步探索阶段，对现实世界实践的理解有限，这造成了学术界与工业界之间的鸿沟，并阻碍了大语言模型在无人机中的应用。为解决这一问题，我们开展了首个实证研究，探讨大语言模型如何支持无人机。为了刻画现实世界无人机-大语言模型实践中的常见任务和应用场景，我们进行了一项大规模实证研究，涉及997篇研究论文和1,509个GitHub项目。研究结果将大语言模型在现实世界无人机项目中承担的四个工作流（如信息输入）中的九类常见任务（如自然语言指令解析）进行了分类，并揭示了科研工作与行业实践在任务分布上的巨大差异。为更深入地理解这些差异以及开发者对大语言模型应用于无人机的看法，我们对从业者进行了一项问卷调查，收到了来自15个国家的52份有效回复。结果表明，尽管40.4%的开发者已尝试将大语言模型应用于无人机任务，但59.6%的开发者仍面临将无人机项目与先进大语言模型能力整合的挑战。他们的反馈将这些挑战归因于技术成熟度、性能、安全性、成本等五个因素，并为研究人员和开发者在开展无人机-大语言模型实践方面提供了实用启示。