Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Framework for Controlling Multi-Robot Systems Using Bayesian
  Optimization and Linear Combination of Vectors   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.

26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph