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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2304.03133 |
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| _version_ | 1866929283335716864 |
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| author | Haughn, Kevin PT. Harvey, Christina Inman, Daniel J. |
| author_facet | Haughn, Kevin PT. Harvey, Christina Inman, Daniel J. |
| contents | There is a growing need for uncrewed aerial vehicles (UAVs) to operate in cities. However, the uneven urban landscape and complex street systems cause large-scale wind gusts that challenge the safe and effective operation of UAVs. Current gust alleviation methods rely on traditional control surfaces and computationally expensive modeling to select a control action, leading to a slower response. Here, we used deep reinforcement learning to create an autonomous gust alleviation controller for a camber-morphing wing. This method reduced gust impact by 84%, directly from real-time, on-board pressure signals. Notably, we found that gust alleviation using signals from only three pressure taps was statistically indistinguishable from using six signals. This reduced-sensor fly-by-feel control opens the door to UAV missions in previously inoperable locations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_03133 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Deep learning reduces sensor requirements for gust rejection on a small uncrewed aerial vehicle morphing wing Haughn, Kevin PT. Harvey, Christina Inman, Daniel J. Robotics Systems and Control There is a growing need for uncrewed aerial vehicles (UAVs) to operate in cities. However, the uneven urban landscape and complex street systems cause large-scale wind gusts that challenge the safe and effective operation of UAVs. Current gust alleviation methods rely on traditional control surfaces and computationally expensive modeling to select a control action, leading to a slower response. Here, we used deep reinforcement learning to create an autonomous gust alleviation controller for a camber-morphing wing. This method reduced gust impact by 84%, directly from real-time, on-board pressure signals. Notably, we found that gust alleviation using signals from only three pressure taps was statistically indistinguishable from using six signals. This reduced-sensor fly-by-feel control opens the door to UAV missions in previously inoperable locations. |
| title | Deep learning reduces sensor requirements for gust rejection on a small uncrewed aerial vehicle morphing wing |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2304.03133 |