Autonomous underwater vehicle sampling for hotspots in chlorophyll A

Abstract With the goal of effective chlorophyll mapping in space and time using autonomous underwater vehicles, we study algorithms for hotspot sampling. We develop, implement and test a sampling algorithm using the expected improvement in chlorophyll to evaluate potential sampling designs. Working in a depth and lateral mode, the vehicle intelligently chooses the survey path for each sampling stage. The onboard system for design evaluation and data assimilation builds on a Gaussian random field representation of the log chlorophyll variations in north, east, depth, and time coordinates. Using a grid-free approach with spatio-temporal thinning of data, the random field model is quickly updated with in situ measurements made by the autonomous vehicle. This approach further enables real-time calculations for the expected improvement criterion used for sampling decisions. Via simulation studies, we compare the suggested sampling approach with others. Results of trials in the Mausund area, Norway, are used to demonstrate field deployments of our implementation for adaptive sampling. Here, we also discuss and compare the chlorophyll samples with that of zooplankton data gathered at the same locations.