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We propose a general framework for creating parameterized control schemes fordecentralized multi-robot systems. A variety of tasks can be seen in thedecentralized multi-robot literature, each with many possible control schemes.For several of them, the agents choose control velocities using algorithms thatextract information from the environment and combine that information inmeaningful ways. From this basic formation, a framework is proposed thatclassifies each robots' measurement information as sets of relevant scalars andvectors and creates a linear combination of the measured vector sets. Alongwith an optimizable parameter set, the scalar measurements are used to generatethe coefficients for the linear combination. With this framework and Bayesianoptimization, we can create effective control systems for several multi-robottasks, including cohesion and segregation, pattern formation, andsearching/foraging.

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