UAV-VLA：用于大规模空中任务生成的视觉-语言-行动系统
UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation
AI总结：
提出UAV-VLA系统，融合卫星图像、VLM与GPT，将文本请求转化为无人机飞行路径与行动计划，并通过KNN实验验证轨迹长度与目标定位误差。
摘要：
UAV-VLA（Visual-Language-Action，视觉-语言-行动）系统是一种旨在促进与空中机器人通信的工具。通过将卫星图像处理与视觉语言模型（VLM）以及GPT的强大能力相结合，UAV-VLA使用户能够通过简单的文本请求生成通用的飞行路径与行动计划。该系统利用卫星图像提供的丰富上下文信息，从而增强决策和任务规划。VLM的视觉分析与GPT的自然语言处理相结合，可以为用户提供路径与行动集合，使空中作业更高效、更易使用。新开发的方法显示，所生成轨迹的长度差异为22%；在K-Nearest Neighbors（KNN，K近邻）方法中，在地图上查找感兴趣目标的平均误差按欧氏距离计算为34.22米。
英文摘要：
The UAV-VLA (Visual-Language-Action) system is a tool designed to facilitate communication with aerial robots. By integrating satellite imagery processing with the Visual Language Model (VLM) and the powerful capabilities of GPT, UAV-VLA enables users to generate general flight paths-and-action plans through simple text requests. This system leverages the rich contextual information provided by satellite images, allowing for enhanced decision-making and mission planning. The combination of visual analysis by VLM and natural language processing by GPT can provide the user with the path-and-action set, making aerial operations more efficient and accessible. The newly developed method showed the difference in the length of the created trajectory in 22% and the mean error in finding the objects of interest on a map in 34.22 m by Euclidean distance in the K-Nearest Neighbors (KNN) approach.
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。
