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

协作研究：支持开发自主水下航行器自适应采样算法的模拟环境

• 批准号: 0354810
• 负责人: Daniel Stilwell
• 金额: $ 4.2万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-01-01 至 2005-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0354810&HistoricalAwards=false
• 关键词: Collaborative; Research; Simulation; Environment; Enable

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.

PI 已申请小额探索性研究资助 (SGER)，作为开发自适应采样算法工具的第一阶段，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 认为，自适应采样算法有潜力能够快速、高保真地绘制和跟踪化学和生物羽流、锋面、小规模环流现象以及其他具有科学意义的海洋学特征。这些算法的主要特点是采样策略是由自动驾驶车辆根据实时收集的数据确定的，无需人工干预。与目前的技术水平相比，AUV 在潜在感兴趣的区域运行预先编程的路线，自适应采样有可能显着提高收集数据的质量，并显着减少收集数据所需的时间和费用。