Ocean monitoring is an expensive and time consuming endeavor, but it can be made more efficient through the use of teams of autonomous robots. In this paper, we present a system for the autonomous identification and tracking of ocean fronts by coordinating the sampling efforts of a heterogeneous team of autonomous surface vehicles (ASVs) and autonomous underwater vehicles (AUVs). The primary contributions of this study are (1) our algorithm for performing autonomous coordination using general autonomy principles: Sequential Allocation Monte Carlo Tree Search (SA‐MCTS) which incorporates domain knowledge into the environmental estimation through both augmenting a standard Gaussian process with a nearest neighbors prior and planning in a drifting reference frame, (2) our decision support user interface to help human operators oversee the autonomous system, and (3) the demonstration of the system's operation in a 2‐week long deployment in the Gulf of Mexico using a heterogeneous team of four Slocum gliders and two robotic ocean surface samplers. With these contributions, we aim to bridge the gap between state of the art autonomy algorithms and marine vehicle planning methods that have been tested in large‐scale field trials. This paper presents the first deployment of a general, heuristic‐based, multi‐robot coordination algorithm for an extended sampling mission.

中文翻译：

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使用一组异构海洋车辆进行海洋前沿检测和跟踪

海洋监测是一项昂贵且费时的工作，但是可以通过使用自动机器人团队来提高海洋监测的效率。在本文中，我们通过协调一支由自主水面车辆（ASV）和水下自主车辆（AUV）组成的异类团队的采样工作，提出了一种用于自动识别和跟踪海沿的系统。这项研究的主要贡献是（1）我们的使用通用自治原则执行自治协调的算法：顺序分配蒙特卡洛树搜索（SA-MCTS），该方法通过将标准高斯过程与最接近点进行增强，从而将领域知识纳入环境估计中在漂移参考系中进行邻居优先和计划，（2）我们的决策支持用户界面，以帮助人类操作员监控自治系统，以及（3）使用由四个Slocum滑翔机和两个机器人组成的异构团队，在墨西哥湾进行为期2周的部署演示系统的操作海洋表面采样器。借助这些贡献，我们旨在弥合最先进的自主算法与已在大规模现场试验中进行测试的航海车辆计划方法之间的差距。本文介绍了用于扩展采样任务的通用，基于启发式，多机器人协调算法的首次部署。我们旨在弥合最先进的自主算法与已在大规模现场试验中测试的海上车辆规划方法之间的差距。本文介绍了用于扩展采样任务的通用，基于启发式，多机器人协调算法的首次部署。我们旨在弥合最先进的自主算法与已在大规模现场试验中测试的海上车辆规划方法之间的差距。本文介绍了用于扩展采样任务的通用，基于启发式，多机器人协调算法的首次部署。