摘要
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 mini-ature autonomous blimps.
