Decentralized Nested Gaussian Processes for Multi-Robot Systems

Abstract

In this paper, we propose two decentralized approximate algorithms for nested Gaussian processes in multi-robot systems. The distributed implementation is achieved with iterative and consensus methods that facilitate local computations at the expense of inter-robot communications. Moreover, we propose a covariance-based nearest neighbor robot selection strategy that enables a subset of agents to perform predictions. In addition, both algorithms are proved to be consistent. Empirical evaluations with real data illustrate the efficiency of the proposed algorithms.