Abstract

The number of unmanned aerial system (UAS) applications for supporting rescue forces is growing in recent years. The analysis of sensed information and control of the UAV creates an enormous psychological and emotional load for the involved humans especially in critical and hectic situations. To enable a mission-guided application of drones and reduce the load of manual control and analysis, the research project EffFeu (Efficient Operation of Unmanned Aerial Vehicle for Industrial Fire Fighters) aims for a holistic integration of UAS in the daily work of industrial fire fighters in particular. This work presents the current stage of the project including the overall system architecture and first results of the project research topics: high-level task control of UAV, localisation and navigation in the transition of indoor and outdoor environments, and objects and situation recognition.

@inproceedings{Hrabia:2018:EPE:3213526.3213533,
 author = {Hrabia, Christopher-Eyk and Hessler, Axel and Xu, Yuan and Brehmer, Jan and Albayrak, Sahin},
 title = {EffFeu Project: Efficient Operation of Unmanned Aerial Vehicles for Industrial Fire Fighters},
 booktitle = {Proceedings of the 4th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications},
 series = {DroNet'18},
 year = {2018},
 isbn = {978-1-4503-5839-2},
 location = {Munich, Germany},
 pages = {33--38},
 numpages = {6},
 url = {http://doi.acm.org/10.1145/3213526.3213533},
 doi = {10.1145/3213526.3213533},
 acmid = {3213533},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {GNSS-denied localisation, decision-making, decisional autonomy, deep learning, object recognition, planning},
}