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This article presents a novel dataset focused on structural damage in quadcopters, addressing a significant gap in unmanned aerial vehicle (UAV or drone) research. The dataset is called CrazyPAD (Crazyflie Propeller Anomaly Data) according to the name of the Crazyflie 2.1 nano-quadrocopter used to collect the data. Despite the existence of datasets on UAV anomalies and behavior, none of them covers structural damage specifically in nano-quadrocopters. Our dataset, therefore, provides critical data for developing predictive models for defect detection in nano-quadcopters. This work details the data collection methodology, involving rigorous simulations of structural damages and their effects on UAV performance. The ultimate goal is to enhance UAV safety by enabling accurate defect diagnosis and predictive maintenance, contributing substantially to the field of UAV technology and its practical applications.
Masalimov et al. (Thu,) studied this question.
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