深層学習によるUAVプロペラ故障のシミュレーション・実機間診断
Simulation-to-reality UAV Fault Diagnosis with Deep Learning
シミュレーションで学習した深層学習モデルをドメイン適応と新特徴量で実機ドローンのプロペラ故障診断に応用し、96%の精度を達成した。
著者: Wei Zhang, Junjie Tong, Fang Liao, Yunfeng Zhang
分類: cs.RO, cs.AI
原文アブストラクト
Accurate diagnosis of propeller faults is crucial for ensuring the safe and efficient operation of quadrotors. Training a fault classifier using simulated data and deploying it on a real quadrotor is a cost-effective and safe approach. However, the simulation-to-reality gap often leads to poor performance of the classifier when applied in real flight. In this work, we propose a deep learning model that addresses this issue by utilizing newly identified features (NIF) as input and utilizing domain adaptation techniques to reduce the simulation-to-reality gap. In addition, we introduce an adjusted simulation model that generates training data that more accurately reflects the behavior of real quadrotors. The experimental results demonstrate that our proposed approach achieves an accuracy of 96\% in detecting propeller faults. To the best of our knowledge, this is the first reliable and efficient method for simulation-to-reality fault diagnosis of quadrotor propellers.
