Markov Random Fields Models for Multi-Robot Teams in Cyber-Physical Systems

We propose Markov random fields (MRFs) as a probabilistic mathematical model for unifying approaches to coordination among multi-robot and cyber-physical systems or, more specifically, distributed action selection. The MRF model is well-suited to domains in which the joint probability over latent (action) and observed (perceived) variables can be factored into pairwise interactions between these variables. Specifically, these interactions occur through functions that evaluate “local evidence” between an observed and latent variable and “compatibility” between a pair of latent variables. For multi-robot coordination, we cast local evidence functions as the computation for an individual robot’s action selection from its local observations and compatibility as the dependence in action selection between a pair of robots. We describe how existing methods for multi-robot coordination (or at least a non-exhaustive subset) fit within an MRF-based model and how they conceptually unify.