Increasingly being used to explore and observe the world's oceans, AUVs can help determine where, when, and what to sample for the most informative data, and how to optimally reach sampling locations. With that in mind, MIT researchers have 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, say the researchers, they 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. If the degree of "mutual information" between two such variables is high, an AUV can be programmed to go to certain locations to measure one variable, to gain information about the other.
"Not all data are equal," says Arkopal Dutt, a graduate student in MIT's Department of Mechanical Engineering. "Our criteria allow the autonomous machines to pinpoint sensor locations and sampling times where the most informative measurements can be made."
To determine how to safely and efficiently reach ideal sampling destinations, the researchers developed a way to help AUVs use the uncertain ocean's activity by forecasting out a dynamic three-dimensional region of the ocean that an AUV would be guaranteed to reach within a certain time, given the vehicle's power constraints and the ocean's currents. The team's method enables a vehicle to surf currents that would bring it closer to its destination, and avoid those that would throw it off track.
"AUVs are not very fast, and their autonomy is not infinite, so you really have to take into account the currents and their uncertainties, and model things rigorously," says Pierre Lermusiaux, professor of mechanical engineering and ocean science and engineering at MIT, who led the research. "Machine intelligence for these autonomous systems comes from rigorously deriving and merging governing differential equations and