A Framework for Controlling Multi-Robot Systems Using Bayesian Optimization and Linear Combination of Vectors

Abstract

We propose a general framework for creating parameterized control schemes for decentralized multi-robot systems. A variety of tasks can be seen in the decentralized multi-robot literature, each with many possible control schemes. For several of them, the agents choose control velocities using algorithms that extract information from the environment and combine that information in meaningful ways. From this basic formation, a framework is proposed that classifies each robots' measurement information as sets of relevant scalars and vectors and creates a linear combination of the measured vector sets. Along with an optimizable parameter set, the scalar measurements are used to generate the coefficients for the linear combination. With this framework and Bayesian optimization, we can create effective control systems for several multi-robot tasks, including cohesion and segregation, pattern formation, and searching/foraging.