TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency
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| Autores principales: | , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| author | Cani, Jorgen Koletsis, Panagiotis Foteinos, Konstantinos Kefaloukos, Ioannis Argyriou, Lampros Falelakis, Manolis Del Pino, Iván Santamaria-Navarro, Angel Čech, Martin Severa, Ondřej Umbrico, Alessandro Fracasso, Francesca Orlandini, AndreA Drakoulis, Dimitrios Markakis, Evangelos Varlamis, Iraklis Papadopoulos, Georgios Th. |
| author_facet | Cani, Jorgen Koletsis, Panagiotis Foteinos, Konstantinos Kefaloukos, Ioannis Argyriou, Lampros Falelakis, Manolis Del Pino, Iván Santamaria-Navarro, Angel Čech, Martin Severa, Ondřej Umbrico, Alessandro Fracasso, Francesca Orlandini, AndreA Drakoulis, Dimitrios Markakis, Evangelos Varlamis, Iraklis Papadopoulos, Georgios Th. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09379 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency Cani, Jorgen Koletsis, Panagiotis Foteinos, Konstantinos Kefaloukos, Ioannis Argyriou, Lampros Falelakis, Manolis Del Pino, Iván Santamaria-Navarro, Angel Čech, Martin Severa, Ondřej Umbrico, Alessandro Fracasso, Francesca Orlandini, AndreA Drakoulis, Dimitrios Markakis, Evangelos Varlamis, Iraklis Papadopoulos, Georgios Th. Robotics Artificial Intelligence 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. |
| title | TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2502.09379 |