SkyVLN：城市环境中无人机的视觉-语言导航与NMPC控制
SkyVLN: Vision-and-Language Navigation and NMPC Control for UAVs in Urban Environments
AI总结：
SkyVLN将视觉-语言导航与非线性模型预测控制结合，利用大语言模型解析指令，提升无人机在城市环境中的导航准确性与鲁棒性。
摘要：
无人机（UAV）凭借其机动性和适应性，已成为各行业的多功能工具。本文介绍了SkyVLN，一种将视觉-语言导航（VLN）与非线性模型预测控制（NMPC）相结合的新型框架，旨在增强无人机在复杂城市环境中的自主性。与传统导航方法不同，SkyVLN利用大型语言模型（LLMs）来解析自然语言指令和视觉观察，使无人机能够在动态三维空间中导航，并具有更高的准确性和鲁棒性。我们提出了一种多模态导航智能体，配备细粒度空间语言化器和历史路径记忆机制。这些组件使无人机能够消除空间上下文歧义，处理模糊指令，并在必要时回溯。该框架还集成了用于动态避障的NMPC模块，确保精确的轨迹跟踪和碰撞预防。为验证我们的方法，我们使用AirSim开发了一个高保真三维城市仿真环境，具有逼真的图像和动态城市元素。大量实验表明，SkyVLN显著提高了导航成功率和效率，尤其是在新的未见环境中。
英文摘要：
Unmanned Aerial Vehicles (UAVs) have emerged as versatile tools across various sectors, driven by their mobility and adaptability. This paper introduces SkyVLN, a novel framework integrating vision-and-language navigation (VLN) with Nonlinear Model Predictive Control (NMPC) to enhance UAV autonomy in complex urban environments. Unlike traditional navigation methods, SkyVLN leverages Large Language Models (LLMs) to interpret natural language instructions and visual observations, enabling UAVs to navigate through dynamic 3D spaces with improved accuracy and robustness. We present a multimodal navigation agent equipped with a fine-grained spatial verbalizer and a history path memory mechanism. These components allow the UAV to disambiguate spatial contexts, handle ambiguous instructions, and backtrack when necessary. The framework also incorporates an NMPC module for dynamic obstacle avoidance, ensuring precise trajectory tracking and collision prevention. To validate our approach, we developed a high-fidelity 3D urban simulation environment using AirSim, featuring realistic imagery and dynamic urban elements. Extensive experiments demonstrate that SkyVLN significantly improves navigation success rates and efficiency, particularly in new and unseen environments.
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。
