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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2302.08117 |
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| _version_ | 1866910499147350016 |
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| author | Zhang, Wei Wang, Shanze Tong, Junjie Liao, Fang Zhang, Yunfeng Shen, Xiaoyu |
| author_facet | Zhang, Wei Wang, Shanze Tong, Junjie Liao, Fang Zhang, Yunfeng Shen, Xiaoyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_08117 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DDCNN: A Promising Tool for Simulation-To-Reality UAV Fault Diagnosis Zhang, Wei Wang, Shanze Tong, Junjie Liao, Fang Zhang, Yunfeng Shen, Xiaoyu Robotics 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. |
| title | DDCNN: A Promising Tool for Simulation-To-Reality UAV Fault Diagnosis |
| topic | Robotics |
| url | https://arxiv.org/abs/2302.08117 |