RI: Medium: Collaborative Research: Decision-Making on Uncertain Spatial-Temporal Fields: Modeling, Planning and Control with Applications to Adaptive Sampling

RI：中：协作研究：不确定时空场的决策：建模、规划和控制及其在自适应采样中的应用

• 批准号: 1302360
• 负责人: Marin Kobilarov
• 金额: $ 19.96万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1302360&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Decision

Inland bodies of freshwater are a resource that is critical for the Nation's health and safety. This project is developing a new spatio-temporal field representation suitable for modeling, planning and control under uncertainty in order to improve monitoring of such water systems. The project's focus is on a reconfigurable aquatic sensor-actuator network designed to capture data from coupled physical, chemical, and biological processes that occur across space and time-scales. The key advantages of this sensor-actuator network in its application to this domain include synoptic volume coverage, adaptive sampling, flexible control and robustness to component failure. The research objective is to build models of dynamic processes for which high resolution sampling is necessary at special locations. Toward this end, this project is contributing new methods, data-structures, algorithms, and implementations validated by field testing a heterogeneous system consisting of stationary and mobile (robotic) underwater node. This project provides unique interdisciplinary opportunities for education of both graduate and undergraduate students via new course work that blends projects and research topics directly into courses and newly developed seminars. It provides a multi-disciplinary experience for students while developing their engineering skills. Relevant components of computer science, computer engineering, and mechanical engineering are integrated together by using the project's aquatic platform and experimental scenarios as a focal point. The project advances the state-of-the-art for such systems because it integrates low-level dynamic processes with high-level planning and distributed optimization. The research represents a change in the scale of robotic aquatic sampling away from immense bodies ofwater in oceanographic research, toward bodies of water that have a more immediate affect on our well-being as they are sources and stores of drinking water. The impact of datasets which lead to better understanding of managed and natural inlets, differing topography including dam walls and man-made structures, regions of turbulence, and seasonal algal growth are immense.

内陆淡水体是对国家健康和安全至关重要的资源。 该项目正在开发一种新的时空场表示，适用于不确定性下的建模、规划和控制，以改善对此类水系统的监测。该项目的重点是可重构的水生传感器执行器网络，旨在捕获跨空间和时间尺度发生的耦合物理、化学和生物过程的数据。 该传感器-执行器网络在该领域的应用中的主要优势包括天气体积覆盖、自适应采样、灵活控制和对组件故障的鲁棒性。 研究目标是建立动态过程模型，在特殊位置需要高分辨率采样。为此，该项目正在贡献新的方法、数据结构、算法和实现，通过对由固定和移动（机器人）水下节点组成的异构系统进行现场测试来验证。该项目通过将项目和研究主题直接融入课程和新开发的研讨会的新课程工作，为研究生和本科生的教育提供了独特的跨学科机会。它为学生提供多学科的体验，同时培养他们的工程技能。以该项目的水上平台和实验场景为焦点，将计算机科学、计算机工程和机械工程的相关组成部分集成在一起。该项目推进了此类系统的最先进水平，因为它将低级动态流程与高级规划和分布式优化集成在一起。这项研究代表了机器人水生采样规模的变化，从海洋学研究中的巨大水体，转向对我们的福祉有更直接影响的水体，因为它们是饮用水的来源和储存。数据集的影响是巨大的，可以帮助我们更好地了解管理和自然的水湾、不同的地形（包括坝墙和人造结构）、湍流区域和季节性藻类生长。