EAGER: Navigating Unmanned Underwater Vehicles (UUVs) at the Ice-water Boundary

在北极，海洋中的气-海相互作用和生物-化学过程受到海冰覆盖范围和厚度的强烈影响。目前，出于安全考虑，涉及北极无人水下航行器（UUV）的数据收集通常在远离冰水边界的地方进行，并使用定制系统进行恢复。因此，关键的科学过程（例如，浮游植物大量繁殖和海冰中近海水域的海气交换）的采样不足。在冰覆盖的海洋中，特别是在冰架附近，导航UUV仍然存在挑战。该项目的目标是开发和测试一种用于冰下观测和数据收集的安全、远距离自主紫外线仪器，方法是设计和实施一种新的先进系统，通过使用多个传感器和包括人工智能算法在内的现场决策进行近冰水界面测量。该项目旨在开发和测试一种增强型无人水下航行器（UUV）系统，该系统具有精确的冰相对定位解决方案，现场避碰重新规划机制和强大的水开放检测能力。具体而言，主要目标是开发新的水下自主采样能力，提供关键的测量和观测，以增进我们对冰下生物生产力和空气-冰-水边界物理-化学输运的了解。为了使其适应各种UUV，将选择一套低尺寸，重量，功率和成本（SWAP-C）传感器，并使用开源软件开发算法。系统将通过融合惯性测量和感知信息（例如，冰的地形、结构和气泡）。在冰下作业期间，该系统还将检测冰龙骨和挤压，然后在必要时调整其路径以避免碰撞。最后，所设计的导航系统将执行海冰中的水开口的鲁棒检测。这将允许一个安全的UUV浮出水面事件，用于传输数据，更新使命计划，并在冰开口处收集独特的跨界测量值。在这个项目中，开发的导航系统将集成在一个便携式水下机器人和测试在一个冻结的淡水池塘和亚极地湖泊。一个紧凑的科学传感器套件也将被连接到搜索冰下水华在冰水界面。该项目还支持早期职业科学家，并通过让本科生参与数据处理和分析，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

In the Arctic, air-sea interactions and biological-chemical processes in the ocean are strongly affected by the extent and thickness of sea ice cover. Presently, due to safety concerns, data collection involving Unmanned Underwater Vehicles (UUVs) in the Arctic typically operate far away from the ice-water boundary and are recovered using customized systems. As a consequence, critical scientific processes (e.g., phytoplankton blooms and air-sea exchanges at offshore leads in the sea ice) are under-sampled. Challenges still exist for navigating UUVs in the ice-covered ocean, especially in proximity to the ice shelf. The goal of this project is to develop and test a safe, long-distance autonomous UUV-based instrument for under-ice observations and data collection by designing and implementing a new advanced system for near ice-water interface measurements through the use of multiple sensors and in situ decision-making including artificial intelligence algorithms. This project aims to develop and test an enhanced Unmanned Underwater Vehicle (UUV) system with an accurate ice-relative localization solution, an in-situ collision avoidance re-planning mechanism, and a robust water-opening detection capability. Specifically, the primary objective is to develop new underwater autonomous sampling capabilities that will provide critical measurements and observations for advancing our knowledge about under-ice biological productivity and the physical-chemical transports at the air-ice-water boundary. To make it adaptable to a variety of UUVs, a suite of low size, weight, power, and cost (SWAP-C) sensors will be selected, and the algorithms will be developed using open-source software. The system will localize the vehicle relative to the ice via fusing the inertial measurements and the perception information (e.g., ice topography, texture, and air bubbles). During under-ice operations, the system will also detect ice keels and extrusions, then adapt its path for collision avoidance if necessary. Finally, the designed navigation system will perform a robust detection of water openings in the sea ice. This will allow a safe UUV surfacing event for transmitting data, updating mission plans, and collecting unique cross-boundary measurements at the ice openings. In this project, the developed navigation system will be integrated on a portable underwater robot and tested in a frozen freshwater pond and a subpolar lake. A compact science sensor suite will also be attached to search for under-ice blooms at the ice-water interface. This project also supports an early career scientist and includes a commitment to education and training by involving undergraduate students in data processing and analysis, as well as outreach and science communication activities that engage a broader audience using varied community and media outlets.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.