Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913873133568000 |
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| author | Doering, Andreas Wiggert, Marius Krasowski, Hanna Doshi, Manan Lermusiaux, Pierre F. J. Tomlin, Claire J. |
| author_facet | Doering, Andreas Wiggert, Marius Krasowski, Hanna Doshi, Manan Lermusiaux, Pierre F. J. Tomlin, Claire J. |
| contents | Low-propulsion vessels can take advantage of powerful ocean currents to navigate towards a destination. Recent results demonstrated that vessels can reach their destination with high probability despite forecast errors. However, these results do not consider the critical aspect of safety of such vessels: because of their low propulsion which is much smaller than the magnitude of currents, they might end up in currents that inevitably push them into unsafe areas such as shallow areas, garbage patches, and shipping lanes. In this work, we first investigate the risk of stranding for free-floating vessels in the Northeast Pacific. We find that at least 5.04% would strand within 90 days. Next, we encode the unsafe sets as hard constraints into Hamilton-Jacobi Multi-Time Reachability (HJ-MTR) to synthesize a feedback policy that is equivalent to re-planning at each time step at low computational cost. While applying this policy closed-loop guarantees safe operation when the currents are known, in realistic situations only imperfect forecasts are available. We demonstrate the safety of our approach in such realistic situations empirically with large-scale simulations of a vessel navigating in high-risk regions in the Northeast Pacific. We find that applying our policy closed-loop with daily re-planning on new forecasts can ensure safety with high probability even under forecast errors that exceed the maximal propulsion. Our method significantly improves safety over the baselines and still achieves a timely arrival of the vessel at the destination. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_01917 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers Doering, Andreas Wiggert, Marius Krasowski, Hanna Doshi, Manan Lermusiaux, Pierre F. J. Tomlin, Claire J. Systems and Control Artificial Intelligence Robotics Low-propulsion vessels can take advantage of powerful ocean currents to navigate towards a destination. Recent results demonstrated that vessels can reach their destination with high probability despite forecast errors. However, these results do not consider the critical aspect of safety of such vessels: because of their low propulsion which is much smaller than the magnitude of currents, they might end up in currents that inevitably push them into unsafe areas such as shallow areas, garbage patches, and shipping lanes. In this work, we first investigate the risk of stranding for free-floating vessels in the Northeast Pacific. We find that at least 5.04% would strand within 90 days. Next, we encode the unsafe sets as hard constraints into Hamilton-Jacobi Multi-Time Reachability (HJ-MTR) to synthesize a feedback policy that is equivalent to re-planning at each time step at low computational cost. While applying this policy closed-loop guarantees safe operation when the currents are known, in realistic situations only imperfect forecasts are available. We demonstrate the safety of our approach in such realistic situations empirically with large-scale simulations of a vessel navigating in high-risk regions in the Northeast Pacific. We find that applying our policy closed-loop with daily re-planning on new forecasts can ensure safety with high probability even under forecast errors that exceed the maximal propulsion. Our method significantly improves safety over the baselines and still achieves a timely arrival of the vessel at the destination. |
| title | Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers |
| topic | Systems and Control Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2307.01917 |