Machine Learning in Creating Energy Consumption Model for UAV

Krystian Góra (), Paweł Smyczyński, Mateusz Kujawiński and Grzegorz Granosik ()
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Krystian Góra: Institute of Automatic Control, Lodz University of Technology, 90-537 Lodz, Poland
Paweł Smyczyński: Institute of Automatic Control, Lodz University of Technology, 90-537 Lodz, Poland
Mateusz Kujawiński: Institute of Automatic Control, Lodz University of Technology, 90-537 Lodz, Poland
Grzegorz Granosik: Institute of Automatic Control, Lodz University of Technology, 90-537 Lodz, Poland

Energies, 2022, vol. 15, issue 18, 1-19

Abstract: The growing interest in the utilization of Unmanned Aerial Vehicles (UAVs) demands minimizing the costs of robot maintenance, where one of the main aspects relates to energy consumption. This manuscript presents a novel approach to create an energy consumption model for UAVs. The authors prove, based on experimentally collected data using a drone carrying various payloads, that Machine Learning (ML) algorithms allow to sufficiently accurately estimate a power signal. As opposed to the classical approach with mathematical modeling, the presented method does not require any knowledge about the drone’s construction, thus making it a universal tool. Calculated metrics show the Decision Tree is the most suitable algorithm among eight different ML methods due to its high energy prediction accuracy of at least 97.5% and a short learning time which was equal to 2 ms for the largest dataset.

Keywords: energy consumption model; UAV; mobile robotics; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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