DDCNN: A Promising Tool for Simulation-To-Reality UAV Fault Diagnosis

2023/02/16 by Zhang, Wei, Wang, Shanze, Tong, Junjie +3
#FOS: Computer and information sciences #Robotics (cs.RO)

paper · doi:10.48550/arxiv.2302.08117

Abstract

Identifying the fault in propellers is important to keep quadrotors operating safely and efficiently. The simulation-to-reality (sim-to-real) UAV fault diagnosis methods provide a cost-effective and safe approach to detecting propeller faults. However, due to the gap between simulation and reality, classifiers trained with simulated data usually underperform in real flights. In this work, a novel difference-based deep convolutional neural network (DDCNN) model is presented to address the above issue. It uses the difference features extracted by deep convolutional neural networks to reduce the sim-to-real gap. Moreover, a new domain adaptation (DA) method is presented to further bring the distribution of the real-flight data closer to that of the simulation data. The experimental results demonstrate that the DDCNN+DA model can increase the accuracy from 52.9% to 99.1% in real-world UAV fault detection.

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