Distributed field mapping for mobile sensor teams using a derivative-free optimisation algorithm

Abstract

The authors propose a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field using a Gaussian Process (GP). The authors’ strategy arises by balancing exploration objectives between areas of high error and high variance. As computing high error regions is impossible since the scalar field is unknown, a bio-inspired approach known as Speeding-Up and Slowing-Down is leveraged to track the gradient of the GP error. This approach achieves global field-learning convergence and is shown to be resistant to poor hyperparameter tuning of the GP. This approach is validated in simulations and experiments using 2D wheeled robots and 2D flying miniature autonomous blimps.