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