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.

翻译：水下滑翔机器人已成为海洋采样的重要工具，但其完全自主的长期运行在实践中仍较为罕见。尽管利益相关者呼吁开发工具来管理日益庞大的滑翔器集群，现有方法因无法应对环境不确定性和操作约束而应用有限。本文证明，考虑不确定性的在线导航规划可在大规模实际滑翔任务中部署。我们将问题形式化为随机最短路径马尔可夫决策过程，并提出基于蒙特卡洛树搜索的样本驱动在线规划器。样本由物理信息仿真器生成，该仿真器基于真实滑翔数据校准，既能捕捉控制执行与海流预测的不确定性，又保持计算可行性。该方法集成至Slocum滑翔器的自主系统中，可在每次浮出水面时执行闭环重规划。系统在北海南部两次部署中完成验证，累计约3个月、1000公里，是文献中迄今最长的全自主滑翔器任务。结果表明，与标准直线导航相比，下潜持续时间提升达9.88%，路径长度缩短达16.51%，其中实地部署中路径长度统计显著减少9.55%。