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

TL;DR

This paper presents a comprehensive UAV-based framework for rapid, accurate post-disaster search and rescue, utilizing swarm deployment, multi-modal sensing, and machine learning to improve detection efficiency and coverage.

Contribution

It introduces an integrated UAV swarm architecture with data fusion and machine learning for enhanced post-disaster search and rescue operations.

Findings

Achieves faster coverage of disaster areas compared to traditional methods.

Enhances survivor detection accuracy through multi-modal sensing and data fusion.

Proposes architectural solutions for real-world deployment challenges.

Abstract

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.