arXiv:2410.22982cs.ROcs.AI2024-10被引 16

用无人机群快速全覆盖废墟,智能识别幸存者

PDSR: Efficient UAV Deployment for Swift and Accurate Post-Disaster Search and Rescue

  • 构建多层无人机集群架构,快速部署覆盖灾区
  • 融合多模态感知数据,提升幸存者识别准确率
  • 适合应急救援、智能城市等场景快速响应需求

本文提出一种面向灾后搜救(PDSR)的综合框架,旨在利用无人机(UAV)优化搜救作业。核心目标是提升在各类灾难场景下的感知精度与可用性。通过快速部署配备多样化传感、通信与智能能力的无人机集群,形成集成多种技术的协同系统,实现对掩埋于废墟下人员的高效探测。该框架采用多层集群架构,在真实灾难场景中显著快于传统方法完成区域全覆盖。同时,融合多模态感知数据并结合机器学习进行信息融合,可有效提升检测准确性,确保幸存者精准定位。

原文摘要 · Abstract (English)

This paper introduces a comprehensive framework for Post-Disaster Search and Rescue (PDSR), aiming to optimize search and rescue operations leveraging Unmanned Aerial Vehicles (UAVs). The primary goal is to improve the precision and availability of sensing capabilities, particularly in various catastrophic scenarios. Central to this concept is the rapid deployment of UAV swarms equipped with diverse sensing, communication, and intelligence capabilities, functioning as an integrated system that incorporates multiple technologies and approaches for efficient detection of individuals buried beneath rubble or debris following a disaster. Within this framework, we propose architectural solution and address associated challenges to ensure optimal performance in real-world disaster scenarios. The proposed framework aims to achieve complete coverage of damaged areas significantly faster than traditional methods using a multi-tier swarm architecture. Furthermore, integrating multi-modal sensing data with machine learning for data fusion could enhance detection accuracy, ensuring precise identification of survivors.

无人机群灾后搜救多模态融合

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