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The proliferation of unmanned vehicles offers many opportunities for solvingenvironmental sampling tasks with applications in resource monitoring andprecision agriculture. Informative path planning (IPP) includes a family ofmethods which offer improvements over traditional surveying techniques forsuggesting locations for observation collection. In this work, we present anovel solution to the IPP problem by using a coregionalized Gaussian processesto estimate a dynamic scalar field that varies in space and time. Our methodimproves previous approaches by using a composite kernel accounting forspatiotemporal correlations and at the same time, can be readily incorporatedin existing IPP algorithms. Through extensive simulations, we show that ournovel modeling approach leads to more accurate estimations when compared withformerly proposed methods that do not account for the temporal dimension.

Informative path planning for scalar dynamic reconstruction using coregionalized Gaussian processes and a spatiotemporal kernel