Physical sampling of water for off-site analysis is necessary for many applications like monitoring the quality of drinking water in reservoirs, understanding marine ecosystems, and measuring contamination levels in fresh-water systems. In this paper, the focus is on algorithms for efficient measurement and sampling using a multi-robot, data-driven, water-sampling behavior, where autonomous surface vehicles plan and execute water sampling using the chlorophyll density as a cue for plankton-rich water samples. We use two Autonomous Surface Vehicles (ASVs), one equipped with a water quality sensor and the other equipped with a water-sampling apparatus. The ASV with the sensor acts as an explorer, measuring and building a spatial map of chlorophyll density in the given region of interest. The ASV equipped with the water sampling apparatus makes decisions in real time on where to sample the water based on the suggestions made by the explorer robot. We evaluate the system in the context of measuring chlorophyll distributions. We do this both in simulation based on real geophysical data from MODIS measurements, and on real robots in a water reservoir. We demonstrate the effectiveness of the proposed approach in several ways including in terms of mean error in the interpolated data as a function of distance traveled.
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