| Citation: | YANG S H，XU G N，JIA Z Z，et al. Wireless charging coil location method of aircraft based on machine learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（8）：2801-2811 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0006
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Magnetically coupled resonant wireless power transfer technology is widely used in drone charging, where the positioning accuracy of charging coils directly affects charging efficiency. To address the limitation of existing methods that neglect angular misalignment of drones, this paper proposes a coil positioning method based on supervised machine learning, capable of simultaneously detecting positional and angular offsets. The method establishes a dataset using auxiliary coil voltages as features and relative positional/angular offsets as labels, then trains a positioning model via supervised learning regression algorithms. Simulation and experimental validation demonstrate a positional detection accuracy of 1 cm and angular detection accuracy of 1°. By integrating mechanical adjustment mechanisms on the charging pad to translate or rotate the transmitter coil, precise coil alignment is achieved, enhancing system charging efficiency.
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