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

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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.