Title: Data-Driven Learning and Planning for Environmental Sampling,
Abstract: Robots such as autonomous underwater vehicles (AUVs) and autonomous surface
vehicles (ASVs) have been used for sensing and monitoring aquatic environments
such as oceans and lakes. Environmental sampling is a challenging task because
the environmental attributes to be observed can vary both spatially and
temporally, and the target environment is usually a large and continuous domain
whereas the sampling data is typically sparse and limited. The challenges
require that the sampling method must be informative and efficient enough to
catch up with the environmental dynamics. In this paper we present a planning
and learning method that enables a sampling robot to perform persistent
monitoring tasks by learning and refining a dynamic "data map" that models a
spatiotemporal environment attribute such as ocean salinity content. Our
environmental sampling framework consists of two components: to maximize the
information collected, we propose an informative planning component that
efficiently generates sampling waypoints that contain the maximal information;
To alleviate the computational bottleneck caused by large-scale data
accumulated, we develop a component based on a sparse Gaussian Process whose
hyperparameters are learned online by taking advantage of only a subset of data
that provides the greatest contribution. We validate our method with both
simulations running on real ocean data and field trials with an ASV in a lake
environment. Our experiments show that the proposed framework is both accurate
in learning the environmental data map and efficient in catching up with the
dynamic environmental changes.