Online Navigation Planning for Long-term Autonomous Operation of Underwater Gliders

Authors:Victor-Alexandru Darvariu, Charlotte Z. Reed, Jan Stratmann, Bruno Lacerda, Benjamin Allsup, Stephen Woodward, Elizabeth Siddle, Trishna Saeharaseelan, Owain Jones, Dan Jones, Tobias Ferreira, Chloe Baker, Kevin Chaplin, James Kirk, Ashley Iceton-Morris, Ryan D. Patmore, Jeff Polton, Charlotte Williams, Christopher D. J. Auckland, Rob A. Hall, Alexandra Kokkinaki, Alvaro Lorenzo Lopez, Justin J. H. Buck, Nick Hawes View PDF HTML (experimental) Abstract:Underwater glider robots have become indispensable for ocean sampling, yet fully autonomous long-term operation remains rare in practice. Although stakeholders are calling for tools to manage increasingly large fleets of gliders, existing methods have seen limited adoption due to their inability to account for environmental uncertainty and operational constraints. In this work, we demonstrate that uncertainty-aware online navigation planning can be deployed in real-world glider missions at scale. We formulate the problem as a stochastic shortest-path Markov Decision Process and propose a sample-based online planner based on Monte Carlo Tree Search. Samples are generated by a physics-informed simulator calibrated on real-world glider data that captures uncertain execution of controls and ocean current forecasts while remaining computationally tractable. Our methodology is integrated into an autonomous system for Slocum gliders that performs closed-loop replanning at each surfacing. The system was validated in two North Sea deployments totalling approximately 3 months and 1000 km, representing the longest fully autonomous glider campaigns in the literature to date. Results demonstrate improvements of up to 9.88% in dive duration and 16.51% in path length compared to standard straight-to-goal navigation, including a statistically significant path length reduction of 9.55% in a field deployment. Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.19315 [cs.RO] (or arXiv:2602.19315v2 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2602.19315 arXiv-issued DOI via DataCite Submission history From: Victor-Alexandru Darvariu [view email] [v1] Sun, 22 Feb 2026 19:34:10 UTC (5,547 KB) [v2] Wed, 15 Apr 2026 15:42:40 UTC (15,648 KB)