arXiv 2501.023412025-03-25cs.ROcs.AI

无人机遇上大语言模型：面向智能低空移动体的综述与展望

UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility

The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；Macau University of Science and Technology（澳门科技大学）；School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；School of Software, Northwestern Polytechnical University（西北工业大学软件学院）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与运载工程学院）；John von Neumann Faculty of Informatics, Obuda University（欧布达大学约翰·冯·诺依曼信息学院）

Yonglin Tian, Fei Lin, Yiduo Li, Tengchao Zhang, Qiyao Zhang, Xuan Fu, Jun Huang, Xingyuan Dai, Yutong Wang, Chunwei Tian, Bai Li, Yisheng Lv, Levente Kovács, Fei-Yue Wang

发表信息：: Information Fusion, Volume 122, 2025, 103158
DOI：: 10.1016/j.inffus.2025.103158

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AI总结：

针对当前无人机依赖人工控制、复杂场景自主性有限的问题，本文综述无人机与大语言模型（LLMs）的融合研究，提出面向智能无人机的路线图，为提升无人机在复杂环境中的智能与适应性提供参考。

摘要：

以无人机（UAVs）为代表的低空移动体在交通、物流、农业等多个领域带来了变革性进展。凭借灵活的视角和快速机动性，无人机拓展了传统系统的感知与行动能力，受到学术界和工业界的广泛关注。然而，当前无人机操作主要依赖人工控制，仅在简单场景具备有限自主性，缺乏应对更复杂环境和任务所需的智能与适应性。大语言模型（LLMs）的出现展现出卓越的问题解决和泛化能力，为提升无人机智能提供了前景广阔的途径。本文探讨LLMs与无人机的融合，首先概述无人机系统的基本组件与功能，随后综述LLM技术的最新进展。接着，系统强调无人机可用的多模态数据资源，这些资源为训练和评估提供关键支持。此外，对无人机与LLMs融合的关键任务和应用场景进行分类与分析。最后，提出面向智能无人机的参考路线图，旨在通过自主感知、记忆、推理和工具利用使无人机实现智能体化。相关资源可在https://github.com/Hub-Tian/UAVs_Meet_LLMs获取。

英文摘要：

Low-altitude mobility, exemplified by unmanned aerial vehicles (UAVs), has introduced transformative advancements across various domains, like transportation, logistics, and agriculture. Leveraging flexible perspectives and rapid maneuverability, UAVs extend traditional systems' perception and action capabilities, garnering widespread attention from academia and industry. However, current UAV operations primarily depend on human control, with only limited autonomy in simple scenarios, and lack the intelligence and adaptability needed for more complex environments and tasks. The emergence of large language models (LLMs) demonstrates remarkable problem-solving and generalization capabilities, offering a promising pathway for advancing UAV intelligence. This paper explores the integration of LLMs and UAVs, beginning with an overview of UAV systems' fundamental components and functionalities, followed by an overview of the state-of-the-art in LLM technology. Subsequently, it systematically highlights the multimodal data resources available for UAVs, which provide critical support for training and evaluation. Furthermore, it categorizes and analyzes key tasks and application scenarios where UAVs and LLMs converge. Finally, a reference roadmap towards agentic UAVs is proposed, aiming to enable UAVs to achieve agentic intelligence through autonomous perception, memory, reasoning, and tool utilization. Related resources are available at https://github.com/Hub-Tian/UAVs_Meet_LLMs.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。