Drone delivery is envisioned to be the delivery mode of the future due to its capability to provide autonomous, end-to-end delivery. Such rapid growth of the drone market necessitates careful checks on drone flight delivery, as a failure in any of a drone’s parts can result in an overestimation of the drone’s battery life, an unexpected increase in delivery time, or even a drone crash. Prior works utilize onboard sensors to detect potential drone failures during flight, which is a reactive approach where the problem may have already occurred. In this work, we propose PADrone , a pre-flight and an automated drone abnormality detection system that leverages contactless radio frequency (RF)-based vibration sensing. PADrone utilizes an end-to-end deep learning pipeline to differentiate various abnormalities in motors, propellers, and other drone’s parts, by leveraging their unique vibration fingerprints . PADrone uses a frequency-modulated continuous wave (FMCW) radar-based RF system to capture these unique drone vibrations using an RF bandwidth of 150 MHz in the industrial, scientific, and medical (ISM) band (5.8 GHz). Our real-world evaluations show that PADrone can classify various drone abnormalities with an average accuracy of 97.5%.

Ghozali Suhariyanto Hadi

PADrone: Pre-flight Abnormalities Detection on Drone via Deep RF Sensing

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