  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.
