Voos, Holger[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Le Traon, Yves[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
21st IEEE International Conference on Emerging Technologies and Factory Automation ETFA 2016
from 06-09-2016 to 09-09-2016
Berlin
Germany
[en] Unmanned Aerial Vehicles ; Machine Laerning ; kNN ; Dynamic Time Warping ; Identification of degradation ; Supervised learning
[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
<br />techniques, allows the precise identification of UAV’s damages.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Automation & Robotics Research Group
Researchers ; Professionals ; General public ; Students
[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
