Bayesian Online Learning for Information-based Multi-Agent Exploration with Unknown Radio Signal Distribution
Jinhong Lim, J.Hyeon Park, Hyejun Kim · IFAC-PapersOnLine · 2017
Exploring an unknown environment with multiple robots is an enabling technology for many useful applications. This paper investigates decentralized motion planning for multi-agent exploration in a field with unknown received signal strength (RSS) distribution. The environment is modelled with a Gaussian process using Bayesian online learning by sharing the information obtained from the measurement history of each robot. Then we use the mean function of the Gaussian process to infer the multiple RSS source locations. The inferred source locations are modelled as the probability distribution using Gaussian mixture-probability hypothesis density (GM-PHD) filter. This modelling enables nonparametric approximation of mutual information between source locations and future robot positions. We combine the variance function of the Gaussian process and the mutual information to design an informative and noise-robust planning algorithm for multiple robots. The experimental performance is analyzed by comparing with the variance-based planning algorithm.