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See detailUAV degradation identification for pilot notification using machine learning techniques
Manukyan, Anush UL; Olivares Mendez, Miguel Angel UL; Bissyande, Tegawendé François D Assise UL et al

in Proceedings of 21st IEEE International Conference on Emerging Technologies and Factory Automation ETFA 2016 (2016, September 06)

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 ... [more ▼]

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. [less ▲]

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