MIT researchers deploy an autonomous underwater vehicle to test new navigation and sensing algorithms.
Observing the world's oceans is increasingly a mission assigned to autonomous underwater vehicles (AUVs) - marine robots that are designed to drift, drive, or glide through the ocean without any real-time input from human operators. Critical questions that AUVs can help to answer are where, when, and what to sample for the most informative data, and how to optimally reach sampling locations. MIT engineers have now developed systems of mathematical equations that forecast the most informative data to collect for a given observing mission, and the best way to reach the sampling sites. With their method, the researchers can predict the degree to which one variable, such as the speed of ocean currents at a certain location, reveals information about some other variable, such as temperature at some other location - a quantity called "mutual information." If the degree of mutual information between two variables is high, an AUV can be programmed to go to certain locations to measure one variable, to gain information about the other. The team used their equations and an ocean model they developed, called Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS), in sea experiments to successfully forecast fields of mutual information and guide actual AUVs. "Not all data are equal," says Arkopal Dutt, a graduate student in MIT's Department of Mechanical Engineering. "Our criteria..
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