Reference : UAV degradation identification for pilot notification using machine learning techniques
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/32873
UAV degradation identification for pilot notification using machine learning techniques
English
Manukyan, Anush [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Olivares Mendez, Miguel Angel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Bissyande, Tegawendé François D Assise [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Voos, Holger mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
Le Traon, Yves [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
Sep-2016
IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, Germany, 2016
Yes
No
International
IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) 2016
Sept 2016
Berlin
Germany
[en] machine learning ; UAV ; fault detection
[en] Unmanned Aerial Vehicles are currently investigated
as an important sub-domain of robotics, a fast growing and truly
multidisciplinary research field. UAVs are increasingly deployed
in real-world settings for missions in dangerous environments
or in environments which are challenging to access. Combined
with autonomous flying capabilities, many new possibilities, but
also challenges, open up. To overcome the challenge of early
identification of degradation, machine learning based on flight
features is a promising direction. Existing approaches build
classifiers that consider their features to be correlated. This
prevents a fine-grained detection of degradation for the different
hardware components. This work presents an approach where
the data is considered uncorrelated and, using machine learning
techniques, allows the precise identification of UAV’s damages.
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/32873
10.1109/ETFA.2016.7733537

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