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The increasing frequency and severity of natural disasters in Brazil, particularly in 2024, highlight the urgent need for innovative solutions in Search and Rescue (SAR) operations. This work presents an approach which integrates Retrieval-Augmented Generation (RAG) techniques with Unmanned Aerial Vehicles (UAVs) to enhance real-time data processing, usability, and operator decision-making. By incorporating advanced technologies such as FrameNet Brasil, Robot Operating System 2 (ROS2), and Large Language Models (LLMs), the system transforms UAV-captured data into actionable insights accessible through natural language interfaces. Testing demonstrates its ability to improve situational awareness, identify critical points of interest, and streamline mission execution. This modular and scalable approach lays the groundwork for future advancements in SAR technologies and their application in disaster-prone regions.

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Using UAVs and Retrieval Augmented Generation for Situational Awareness in Rescue Operations

  • 会议论文

  • 发表日期：

    2025年

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  • Matheus B. Jenevain,
  • Milena F. Pinto,
  • Mario A. R. Dantas,
  • Laís R. Berno

摘要

The increasing frequency and severity of natural disasters in Brazil, particularly in 2024, highlight the urgent need for innovative solutions in Search and Rescue (SAR) operations. This work presents an approach which integrates Retrieval-Augmented Generation (RAG) techniques with Unmanned Aerial Vehicles (UAVs) to enhance real-time data processing, usability, and operator decision-making. By incorporating advanced technologies such as FrameNet Brasil, Robot Operating System 2 (ROS2), and Large Language Models (LLMs), the system transforms UAV-captured data into actionable insights accessible through natural language interfaces. Testing demonstrates its ability to improve situational awareness, identify critical points of interest, and streamline mission execution. This modular and scalable approach lays the groundwork for future advancements in SAR technologies and their application in disaster-prone regions.