  This paper reports a new hierarchical architecture for modeling autonomous
multi-robot systems (MRSs): a non-linear dynamical opinion process is used to
model high-level group choice, and multi-objective behavior optimization is
used to model individual decisions. Using previously reported theoretical
results, we show it is possible to design the behavior of the MRS by the
selection of a relatively small set of parameters. The resulting behavior -
both collective actions and individual actions - can be understood intuitively.
The approach is entirely decentralized and the communication cost scales by the
number of group options, not agents. We demonstrated the effectiveness of this
approach using a hypothetical `explore-exploit-migrate' scenario in a two hour
field demonstration with eight unmanned surface vessels (USVs). The results
from our preliminary field experiment show the collective behavior is robust
even with time-varying network topology and agent dropouts.
