UAV-VLRR：用于无人机搜索救援快速响应的视觉-语言信息驱动 NMPC
UAV-VLRR: Vision-Language Informed NMPC for Rapid Response in UAV Search and Rescue
AI总结：
提出融合 VLM、ChatGPT-4o 与带避障 NMPC 的 UAV-VLRR 系统，以自然语言规划搜救任务并安全快速执行，响应速度显著优于自动驾驶仪和人类飞行员。
摘要：
紧急搜索救援（SAR）行动通常需要在复杂环境中快速、精确地识别目标，而传统的人工无人机控制在这类环境中效率较低。为应对这些场景，本研究开发了一种快速 SAR 系统 UAV-VLRR（Vision-Language-Rapid-Response，视觉-语言-快速响应）。该系统包含两个方面：1）一个多模态系统，利用视觉语言模型（VLM）的能力以及 ChatGPT-4o（LLM）的自然语言处理能力进行场景解读。2）一个内置避障功能的非线性模型预测控制（NMPC）模块，用于使无人机根据多模态系统的输出飞行并实现快速响应。本工作旨在通过为操作员提供更直观、更自然的方式来规划 SAR 任务，同时允许无人机快速且安全地执行该任务，从而缩短紧急 SAR 行动中的响应时间。测试结果表明，与现成的自动驾驶仪相比，本文方法平均快 33.75%；与人类飞行员相比，平均快 54.6%。UAV-VLRR 视频：https://youtu.be/KJqQGKKt1xY
英文摘要：
Emergency search and rescue (SAR) operations often require rapid and precise target identification in complex environments where traditional manual drone control is inefficient. In order to address these scenarios, a rapid SAR system, UAV-VLRR (Vision-Language-Rapid-Response), is developed in this research. This system consists of two aspects: 1) A multimodal system which harnesses the power of Visual Language Model (VLM) and the natural language processing capabilities of ChatGPT-4o (LLM) for scene interpretation. 2) A non-linearmodel predictive control (NMPC) with built-in obstacle avoidance for rapid response by a drone to fly according to the output of the multimodal system. This work aims at improving response times in emergency SAR operations by providing a more intuitive and natural approach to the operator to plan the SAR mission while allowing the drone to carry out that mission in a rapid and safe manner. When tested, our approach was faster on an average by 33.75% when compared with an off-the-shelf autopilot and 54.6% when compared with a human pilot. Video of UAV-VLRR: https://youtu.be/KJqQGKKt1xY
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。
