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[2602.19315] Online Navigation Planning for Long-term Autonomous Operation of Underwater Gliders
Summary
This article presents a novel approach to online navigation planning for underwater gliders, utilizing a stochastic shortest-path Markov Decision Process and a Monte Carlo Tree Search-based planner to enhance autonomous operations in marine environments.
Why It Matters
As underwater gliders are crucial for ocean sampling, improving their autonomous navigation capabilities is essential for effective long-term deployments. This research addresses current limitations in fleet management and operational efficiency, contributing to advancements in marine robotics and environmental monitoring.
Key Takeaways
- Introduces a sample-based online planner for underwater gliders.
- Utilizes a physics-informed simulator for navigation planning.
- Demonstrates improved efficiency over traditional navigation methods.
- Validated through field deployments totaling 1000 km of operation.
- Addresses the need for better management of large fleets of gliders.
Computer Science > Robotics arXiv:2602.19315 (cs) [Submitted on 22 Feb 2026] Title: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 Morris, Ryan Patmore, Jeff Polton, Charlotte Williams, Alexandra Kokkinaki, Alvaro Lorenzo Lopez, Justin J. H. Buck, Nick Hawes View a PDF of the paper titled Online Navigation Planning for Long-term Autonomous Operation of Underwater Gliders, by Victor-Alexandru Darvariu and 21 other authors View PDF HTML (experimental) Abstract:Underwater glider robots have become an indispensable tool for ocean sampling. Although stakeholders are calling for tools to manage increasingly large fleets of gliders, successful autonomous long-term deployments have thus far been scarce, which hints at a lack of suitable methodologies and systems. In this work, we formulate glider navigation planning 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 that captures uncertain execution of controls and ocean current forecasts while remaining computationally tractable. The simulator parameters are fitted using historical glider data. We integrate these methods...