Abstract
This paper proposes a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field. The algorithm is based on a bio-inspired approach known as Speeding-Up and Slowing-Down (SUSD) for distributed source seeking problems. Our algorithm leverages a Gaussian Process model to predict field values as robots explore. By comparing Gaussian Process predictions with measurements of the field, agents search along the gradient of the model error while simultaneously improving the Gaussian Process model. We provide a proof of convergence to the gradient direction and demonstrate our approach in simulation and experiments using 2D wheeled robots and 2D flying autonomous miniature blimps.
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