DDCNN：シミュレーションから実環境へのUAV故障診断を実現する有望な手法
DDCNN: A Promising Tool for Simulation-To-Reality UAV Fault Diagnosis
シミュレーションと実環境のギャップを差分特徴とドメイン適応で埋め、実飛行でのプロペラ故障診断精度を52.9%から99.1%に向上させた。
著者: Wei Zhang, Shanze Wang, Junjie Tong, Fang Liao, Yunfeng Zhang, Xiaoyu Shen
分類: cs.RO
原文アブストラクト
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.
