RI: Small: Collaborative Research:Adaptive Sampling with Robots for Marine Observations

RI：小型：协作研究：海洋观测机器人自适应采样

• 批准号: 1116221
• 负责人: Carrick Detweiler
• 金额: $ 25万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116221&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Adaptive

This project develops (1) develops hardware platforms for persistent underwater observations, (2) develops adaptive sampling algorithms, (3) develops efficient data storage and access algorithms for sensor networks with slow broadcast rates, and (4) supports investigation of specific important environmental issues such as the interaction of rainfall events through the landscape to deliver sediment and nutrients to the near-shore and lagoon, ultimately affecting the coral reef. Specifically, the project contributes a new class of modular underwater robots and systems with increased agility in motion and effective data collection and retrieval and a science-base for coordinating underwater robots and sensors to provide a provably-correct foundation for applications to marine observations. Novel decentralized algorithms coordinate a group of robots and sensors for three related problems: sensor placement, event detection and tracking, and adaptive sampling, along with a unified framework for analyzing the stability and convergence of these algorithms.Persistent underwater monitoring is deployable in any coastal environment and provides automation for collecting data in support of many environmental hypotheses. The same capabilities can also be used in the context of coastal and harbor protection operation, locating underwater mines and identifying intrusion. The underwater technology enables an unprecedented level of automation for environmental monitoring in water. The project impacts education through instructional and outreach activities aimed at developing and sharing a new curriculum that brings robotics technology together with marine biology through application, workshops, and tools. The project contributes designs and software for affordable and usable underwater robot and sensor platforms.

该项目开发（1）开发用于持续水下观测的硬件平台，（2）开发自适应采样算法，（3）开发用于慢速广播速率的传感器网络的高效数据存储和访问算法，以及（4）支持对特定重要环境问题的调查，例如降雨事件通过景观的相互作用将沉积物和营养物输送到近岸和泻湖，最终影响珊瑚礁具体而言，该项目提供了一种新型模块化水下机器人和系统，其运动灵活性更高，数据收集和检索更有效，并为协调水下机器人和传感器提供了科学基础，为海洋观测应用提供了可证明正确的基础。新的分散式算法协调一组机器人和传感器的三个相关的问题：传感器放置，事件检测和跟踪，自适应采样，沿着与一个统一的框架，用于分析这些algorithm.Persistent水下监测的稳定性和收敛性是部署在任何沿海环境，并提供自动化收集数据，支持许多环境假设。同样的能力也可以用于海岸和港口保护行动，定位水下地雷和识别入侵。水下技术使水中环境监测的自动化水平达到前所未有的水平。该项目通过旨在开发和分享新课程的教学和推广活动影响教育，通过应用，研讨会和工具将机器人技术与海洋生物学结合在一起。该项目为负担得起和可用的水下机器人和传感器平台提供设计和软件。