Disaster Response Scenario
Disaster response research focuses on improving the speed and effectiveness of search and rescue (SAR) operations, particularly in hazardous environments like those following CBRN events or natural disasters. Current efforts concentrate on developing and integrating autonomous robots (UGVs and UAVs) equipped with advanced sensors and AI-powered human detection systems, leveraging machine learning models trained on specialized datasets to enhance situational awareness and decision-making. These advancements, along with improved communication strategies (e.g., coded caching) and human-robot interaction designs that foster trust and reduce operator cognitive load, aim to significantly improve the safety and efficiency of disaster response.
Papers
Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices
Santino Nanini, Mariem Abid, Yassir Mamouni, Arnaud Wiedemann, Philippe Jouvet, Stephane Bourassa
PDSR: Efficient UAV Deployment for Swift and Accurate Post-Disaster Search and Rescue
Alaa Awad Abdellatif, Ali Elmancy, Amr Mohamed, Ahmed Massoud, Wadha Lebda, Khalid K. Naji