TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency
Abstract
The increasing complexity of natural disaster incidents demands innovative technological solutions to support first responders in their efforts. This paper introduces the TRIFFID system, a comprehensive technical framework that integrates unmanned ground and aerial vehicles with advanced artificial intelligence functionalities to enhance disaster response capabilities across wildfires, urban floods, and post-earthquake search and rescue missions. By leveraging state-of-the-art autonomous navigation, semantic perception, and human-robot interaction technologies, TRIFFID provides a sophisticated system composed of the following key components: hybrid robotic platform, centralized ground station, custom communication infrastructure, and smartphone application. The defined research and development activities demonstrate how deep neural networks, knowledge graphs, and multimodal information fusion can enable robots to autonomously navigate and analyze disaster environments, reducing personnel risks and accelerating response times. The proposed system enhances emergency response teams by providing advanced mission planning, safety monitoring, and adaptive task execution capabilities. Moreover, it ensures real-time situational awareness and operational support in complex and risky situations, facilitating rapid and precise information collection and coordinated actions.
Cite
@article{arxiv.2502.09379,
title = {TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency},
author = {Jorgen Cani and Panagiotis Koletsis and Konstantinos Foteinos and Ioannis Kefaloukos and Lampros Argyriou and Manolis Falelakis and Iván Del Pino and Angel Santamaria-Navarro and Martin Čech and Ondřej Severa and Alessandro Umbrico and Francesca Fracasso and AndreA Orlandini and Dimitrios Drakoulis and Evangelos Markakis and Iraklis Varlamis and Georgios Th. Papadopoulos},
journal= {arXiv preprint arXiv:2502.09379},
year = {2025}
}