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 into an autonomous command-and-control system for Slocum gliders that enables closed-loop replanning at each surfacing. The resulting system was validated in two field deployments in the North Sea totalling approximately 3 months and 1000 km of autonomous operation. Results demonstrate improved efficiency compared to straight-to-goal navigation and show the practicality of sample-based planning for long-term marine autonomy.

翻译：水下滑翔机器人已成为海洋采样不可或缺的工具。尽管相关方呼吁开发工具以管理日益庞大的滑翔机器人集群，但迄今为止成功的长期自主部署仍较为罕见，这暗示了缺乏合适的方法论与系统。在本工作中，我们将滑翔机器人导航规划建模为随机最短路径马尔可夫决策过程，并提出一种基于蒙特卡洛树搜索的采样在线规划器。采样由物理信息模拟器生成，该模拟器在保持计算可行性的同时，能捕捉控制执行的不确定性与洋流预报。模拟器参数通过历史滑翔机器人数据进行拟合。我们将这些方法集成至Slocum滑翔机器人的自主指挥控制系统中，实现了每次上浮时的闭环重规划。所构建的系统在北海的两次实地部署中得到验证，累计实现约3个月、1000公里的自主运行。结果表明，与直线航向目标导航相比，该系统提升了航行效率，并证明了基于采样的规划方法在长期海洋自主运行中的实用性。