Collaborative Research: A Simulation Environment to Enable Development of Adaptive Sampling Algorithms for Autonomous Underwater Vehicles

PIs已经申请了探索性研究（SGER）的小额赠款，作为自适应采样算法工具开发的第一阶段，该工具将使自主水下航行器（AUV）能够确定收集科学数据的最佳位置、路线和时间。pi相信自适应采样算法有潜力实现对化学和生物羽流、锋面、小尺度环流现象和其他科学兴趣的海洋学特征的快速、高保真测绘和跟踪。这些算法的关键特点是，采样策略由自动驾驶汽车根据实时收集的数据确定，无需人工干预。目前的技术水平是，auv在潜在兴趣区域运行预先编程的路线，与此相反，自适应采样有可能显著提高收集数据的质量，并显著减少收集数据所需的时间和费用。

The PIs have requested a Small Grant for Exploratory Research (SGER) as the first stage in the development of an adaptive sampling algorithms tool which would enable an autonomous underwater vehicle (AUV) to determine the best locations, routes, and times to collect scientific data. The PIs believe that adaptive sampling algorithms have the potential to enable rapid, high fidelity mapping and tracking of chemical and biological plumes, fronts, small scale circulation phenomena, and other oceanographic features of scientific interest. The key feature of these algorithms is that sampling strategies are determined by autonomous vehicles based on data collected in real-time and without human intervention. In contrast to the current state of the art, in which AUVs run pre-programmed routes through areas of potential interest, adaptive sampling has the potential to dramatically increase the quality of collected data and to significantly decrease the time and expense required to collected it.