EffFeu Project: Towards Mission-Guided Application of Drones in Safety and Security Environments

Abstract

The number of unmanned aerial system (UAS) applications for supporting rescue forces is growing in recent years. Nevertheless, the analysis of sensed information and control of unmanned aerial vehicle (UAV) creates an enormous psychological and emotional load for the involved humans especially in critical and hectic situations. The introduced research project EffFeu (Efficient Operation of Unmanned Aerial Vehicle for Industrial Firefighters) especially focuses on a holistic integration of UAS in the daily work of industrial firefighters. This is done by enabling autonomous mission-guided control on top of the presented overall system architecture, goal-oriented high-level task control, comprehensive localisation process combining several approaches to enable the transition from and to GNSS-supported and GNSS-denied environments, as well as a deep-learning based object recognition of relevant entities. This work describes the concepts, current stage, and first evaluation results of the research project.

@Article{hrabia2019efffeu,
AUTHOR = {Hrabia, Christopher-Eyk and Hessler, Axel and Xu, Yuan and Seibert, Jacob and Brehmer, Jan and Albayrak, Sahin},
TITLE = {EffFeu Project: Towards Mission-Guided Application of Drones in Safety and Security Environments},
JOURNAL = {Sensors},
VOLUME = {19},
YEAR = {2019},
NUMBER = {4},
ARTICLE-NUMBER = {973},
URL = {http://www.mdpi.com/1424-8220/19/4/973},
ISSN = {1424-8220},
ABSTRACT = {The number of unmanned aerial system (UAS) applications for supporting rescue forces is growing in recent years. Nevertheless, the analysis of sensed information and control of unmanned aerial vehicle (UAV) creates an enormous psychological and emotional load for the involved humans especially in critical and hectic situations. The introduced research project EffFeu (Efficient Operation of Unmanned Aerial Vehicle for Industrial Firefighters) especially focuses on a holistic integration of UAS in the daily work of industrial firefighters. This is done by enabling autonomous mission-guided control on top of the presented overall system architecture, goal-oriented high-level task control, comprehensive localisation process combining several approaches to enable the transition from and to GNSS-supported and GNSS-denied environments, as well as a deep-learning based object recognition of relevant entities. This work describes the concepts, current stage, and first evaluation results of the research project.},
DOI = {10.3390/s19040973}
}
Autoren:
Kategorie:
Journal
Jahr:
2019
Ort:
MDPI Sensors, Special Issue Unmanned Aerial Vehicle Networks, Systems and Applications