Autonomous surface vessels (ASV) represent a promising technology to automate water-quality monitoring of lakes. In this work, we use satellite images as a coarse map and plan sampling routes for the robot. However, inconsistency between the satellite images and the actual lake, as well as environmental disturbances such as wind, aquatic vegetation, and changing water levels can make it difficult for robots to visit places suggested by the prior map. This paper presents a robust route-planning algorithm that minimizes the expected total travel distance given these environmental disturbances, which induce uncertainties in the map. We verify the efficacy of our algorithm in simulations of over a thousand Canadian lakes and demonstrate an application of our algorithm in a 3.7 km-long real-world robot experiment on a lake in Northern Ontario, Canada. Videos are available on our website https://pcctp.github.io/.

翻译：自主水面艇（ASV）为湖泊水质监测自动化提供了一种前景可观的技术。本研究利用卫星图像作为粗略地图，为机器人规划采样路径。然而，卫星图像与实际湖泊之间的不一致性，以及风、水生植被和水位变化等环境干扰，可能使机器人难以抵达先验地图所建议的地点。本文提出一种鲁棒的路径规划算法，该算法在考虑这些导致地图不确定性的环境干扰前提下，最小化期望总航行距离。我们通过对加拿大一千多个湖泊的仿真验证了算法的有效性，并在加拿大安大略省北部一处湖泊进行了长达3.7公里的真实机器人实验，展示了算法的实际应用。相关视频可访问我们的网站 https://pcctp.github.io/ 。