基于多模态大语言模型的无人机蜂群：迈向高效智能的自主空中系统
Multimodal Large Language Models-Enabled UAV Swarm: Towards Efficient and Intelligent Autonomous Aerial Systems
AI总结：
本文探讨将多模态大语言模型与无人机蜂群融合以提升其任务智能性与适应性，分析了多模态大语言模型在目标检测等方面的增强作用，提出森林消防应用案例并讨论了挑战与未来方向。
摘要：
多模态大语言模型（MLLMs）的最新突破赋予了AI系统跨文本、图像和视频流的统一感知、推理与自然语言交互能力。与此同时，无人机（UAV）蜂群正越来越多地被部署在动态、对安全要求严苛的任务中，这类任务需要快速的态势理解与自主适应能力。本文探讨了将MLLMs与无人机蜂群相融合的潜在解决方案，以提升其在多样化任务中的智能性与适应性。具体而言，我们首先概述了无人机与MLLMs的基础架构与功能。随后，我们分析了MLLMs如何在目标检测、自主导航和多智能体协同方面提升无人机系统的性能，同时探索了将MLLMs集成到无人机系统中的解决方案。接着，我们提出了一个聚焦于森林消防的实际案例研究。为充分展现所提框架的能力，我们对人机交互、蜂群任务规划、火情评估与任务执行展开了研究。最后，我们讨论了基于MLLMs的无人机蜂群所面临的挑战与未来研究方向。实验演示视频可在以下网址在线查看：https://youtu.be/zwnB9ZSa5A4。
英文摘要：
Recent breakthroughs in multimodal large language models (MLLMs) have endowed AI systems with unified perception, reasoning and natural-language interaction across text, image and video streams. Meanwhile, Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in dynamic, safety-critical missions that demand rapid situational understanding and autonomous adaptation. This paper explores potential solutions for integrating MLLMs with UAV swarms to enhance the intelligence and adaptability across diverse tasks. Specifically, we first outline the fundamental architectures and functions of UAVs and MLLMs. Then, we analyze how MLLMs can enhance the UAV system performance in terms of target detection, autonomous navigation, and multi-agent coordination, while exploring solutions for integrating MLLMs into UAV systems. Next, we propose a practical case study focused on the forest fire fighting. To fully reveal the capabilities of the proposed framework, human-machine interaction, swarm task planning, fire assessment, and task execution are investigated. Finally, we discuss the challenges and future research directions for the MLLMs-enabled UAV swarm. An experiment illustration video could be found online at https://youtu.be/zwnB9ZSa5A4.
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。
