Application of Machine Learning Methods to Localize Quadcopter Sensor Failures
https://doi.org/10.17586/0021-3454-2023-66-2-125-130
Abstract
The problem of localizing failures of sensors (accelerometer and gyroscope) of an unmanned aerial vehicle of the "quadcopter" type is considered. An algorithm is developed that provides the ability to detect and classify quadcopter sensor failures using machine learning methods. To solve the problem, the following machine learning methods were used: logistic regression, random forest method, LASSO and ridge regression, as well as elastic net. Experimental results obtained in the course of computer simulation confirm the efficiency of the proposed algorithm. A comparative analysis of the used methods of machine learning is performed.
About the Authors
A. A. Kim
ITMO University
Russian Federation
Russian Federation
Stanislav A. Kim — Post-Graduate Student; Faculty of Control Systems and Robotics
St. Petersburg
A. A. Margyn
ITMO University
Russian Federation
Russian Federation
Alexey A. Margun — PhD, Associate Professor; Faculty of Control Systems and Robotics
St. Petersburg
A. A. Pyrkin
ITMO University
Russian Federation
Russian Federation
Anton A. Pyrkin — Dr. Sci., Professor; Faculty of Control Systems and Robotics;
Dean of the Faculty
St. Petersburg
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Review
For citations:
Kim A.A., Margyn A.A., Pyrkin A.A. Application of Machine Learning Methods to Localize Quadcopter Sensor Failures. Journal of Instrument Engineering. 2023;66(2):125-130. (In Russ.) https://doi.org/10.17586/0021-3454-2023-66-2-125-130
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