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

RI：小型：协作研究：利用机器人进行海洋观测自适应采样

• 批准号: 1117178
• 负责人: Daniela Rus
• 金额: $ 25万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing 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=1117178&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）开发了用于持续的水下观察的硬件平台，（2）开发自适应抽样算法，（3）开发有效的数据存储和传感器网络的访问算法，这些网络的广播速度缓慢，（4）支持对特定重要的环境问题的调查，例如通过降雨和降雨的互动，以使降雨范围和降雨的交互作用，使得降雨范围的交互作用，使其陷入困境，使降雨范围与Seanfore的相互作用相互作用，使得降雨范围和局面相互作用，使其陷入困境，使得降雨范围互动，使其造成了降雨界的相互作用。珊瑚礁。具体而言，该项目贡献了一类新的模块化水下机器人和系统，具有提高运动性，有效的数据收集和检索的敏捷性，以及用于协调水下机器人和传感器的科学基础，以为海上观察的应用提供可证明的基础。新颖的分散化算法协调了一组机器人和传感器，以解决三个相关问题：传感器放置，事件检测和跟踪以及自适应抽样，以及一个统一的框架，用于分析这些算法的稳定性和收敛性。各种抗议者在任何沿海环境中都可以在任何沿海环境和自动化数据中进行自动化，以收集许多环境数据。在沿海和港口保护操作，定位水下矿山并确定入侵的背景下，也可以使用相同的功能。水下技术使水中的自动化水平是空前的自动化水平，用于水中的环境监测。该项目通过旨在开发和共享新课程的教学和外展活动影响教育，该课程通过应用，研讨会和工具将机器人技术与海洋生物学一起提供。该项目为负担得起且可用的水下机器人和传感器平台提供了设计和软件。