PDSR：用于快速精准灾后搜救的高效无人机部署
PDSR: Efficient UAV Deployment for Swift and Accurate Post-Disaster Search and Rescue
AI总结：
本研究提出PDSR灾后搜救综合框架，通过部署多层架构的多能力无人机集群，融合多模态感知数据与机器学习，实现受灾区域更快全覆盖与幸存者精准识别，优化灾后搜救效能。
摘要：
本文提出了一套灾后搜救（Post-Disaster Search and Rescue, PDSR）综合框架，旨在利用无人机（Unmanned Aerial Vehicles, UAVs）优化搜救行动，核心目标是提升感知能力的精准度与可用性，尤其是在各类灾难性场景中。该方案的核心是快速部署配备多样化感知、通信与智能能力的无人机集群，作为集成系统融合多项技术与方法，高效探测灾难后被掩埋在瓦砾或废墟下的人员。在该框架内，我们提出了架构解决方案并应对相关挑战，以确保在真实灾难场景中实现最优性能。所提框架采用多层集群架构，旨在比传统方法快得多地实现受灾区域的完全覆盖。此外，将多模态感知数据与机器学习相结合进行数据融合，可提升检测准确率，确保精准识别幸存者。
英文摘要：
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.
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。
