Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers
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Author(s) • • • • •
Doering, Andreas
Wiggert, Marius
Krasowski, Hanna
Doshi, Manan
Lermusiaux, Pierre F.J.
Tomlin, Claire J.
Date Issued
December 13, 2023
Publisher
IEEE
Citation
Doering, Andreas, Wiggert, Marius, Krasowski, Hanna, Doshi, Manan, Lermusiaux, Pierre F.J. et al. 2023. "Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers."
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Author's final manuscript
Abstract
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.
Description
2023 62nd IEEE Conference on Decision and Control (CDC) December 13-15, 2023. Marina Bay Sands, Singapore
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-ShareAlike
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DOI of Published Version
https://doi.org/10.1109/cdc49753.2023.10383383