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)开发用于持续水下观测的硬件平台，(2)开发自适应采样算法，(3)为广播速率较慢的传感器网络开发有效的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，例如降雨事件通过地貌的相互作用，将沉积物和营养物质输送到近岸和泻湖，最终影响珊瑚礁。具体地说，该项目提供了一种新型模块化水下机器人和系统，提高了运动的灵活性和有效的数据收集和检索，并为协调水下机器人和传感器提供了一个科学基础，为应用于海洋观测提供了一个被证明是正确的基础。新的分布式算法协调一组机器人和传感器处理传感器放置、事件检测和跟踪以及自适应采样这三个相关问题，并提供了一个统一的框架来分析这些算法的稳定性和收敛。持久水下监测可以部署在任何沿海环境中，并提供支持许多环境假设的数据收集自动化。同样的能力也可用于海岸和港口保护行动、定位水下水雷和识别入侵。水下技术使水环境监测达到了前所未有的自动化水平。该项目通过教学和外联活动影响教育，旨在开发和分享新课程，通过应用、讲习班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。