We introduce a multi-sensor navigation system for autonomous surface vessels (ASV) intended for water-quality monitoring in freshwater lakes. Our mission planner uses satellite imagery as a prior map, formulating offline a mission-level policy for global navigation of the ASV and enabling autonomous online execution via local perception and local planning modules. A significant challenge is posed by the inconsistencies in traversability estimation between satellite images and real lakes, due to environmental effects such as wind, aquatic vegetation, shallow waters, and fluctuating water levels. Hence, we specifically modelled these traversability uncertainties as stochastic edges in a graph and optimized for a mission-level policy that minimizes the expected total travel distance. To execute the policy, we propose a modern local planner architecture that processes sensor inputs and plans paths to execute the high-level policy under uncertain traversability conditions. Our system was tested on three km-scale missions on a Northern Ontario lake, demonstrating that our GPS-, vision-, and sonar-enabled ASV system can effectively execute the mission-level policy and disambiguate the traversability of stochastic edges. Finally, we provide insights gained from practical field experience and offer several future directions to enhance the overall reliability of ASV navigation systems.

翻译：我们提出了一种用于淡水湖泊水质监测的自主水面航行器（ASV）多传感器导航系统。我们的任务规划器利用卫星图像作为先验地图，离线制定用于ASV全局导航的任务级策略，并通过本地感知和本地规划模块实现自主在线执行。由于风、水生植被、浅水区和水位波动等环境因素，卫星图像与真实湖泊之间的可通行性估计存在不一致性，这构成了重大挑战。因此，我们专门将这些可通行性不确定性建模为图中的随机边，并优化一个最小化预期总行程距离的任务级策略。为执行该策略，我们提出了一种现代本地规划器架构，该架构处理传感器输入，并在不确定可通行性条件下规划路径以执行高层策略。我们的系统在安大略省北部一个湖泊上进行了三次公里级任务测试，结果表明，配备GPS、视觉和声纳的ASV系统能够有效执行任务级策略，并消除随机边的可通行性歧义。最后，我们分享了从实际现场经验中获得的见解，并提出了若干未来方向，以增强ASV导航系统的整体可靠性。