  The collective operation of robots, such as unmanned aerial vehicles (UAVs)
operating as a team or swarm, is affected by their individual capabilities,
which in turn is dependent on their physical design, aka morphology. However,
with the exception of a few (albeit ad hoc) evolutionary robotics methods,
there has been very little work on understanding the interplay of morphology
and collective behavior. There is especially a lack of computational frameworks
to concurrently search for the robot morphology and the hyper-parameters of
their behavior model that jointly optimize the collective (team) performance.
To address this gap, this paper proposes a new co-design framework. Here the
exploding computational cost of an otherwise nested morphology/behavior
co-design is effectively alleviated through the novel concept of ``talent"
metrics; while also allowing significantly better solutions compared to the
typically sub-optimal sequential morphology$\to$behavior design approach. This
framework comprises four major steps: talent metrics selection, talent Pareto
exploration (a multi-objective morphology optimization process), behavior
optimization, and morphology finalization. This co-design concept is
demonstrated by applying it to design UAVs that operate as a team to localize
signal sources, e.g., in victim search and hazard localization. Here, the
collective behavior is driven by a recently reported batch Bayesian search
algorithm called Bayes-Swarm. Our case studies show that the outcome of
co-design provides significantly higher success rates in signal source
localization compared to a baseline design, across a variety of signal
environments and teams with 6 to 15 UAVs. Moreover, this co-design process
provides two orders of magnitude reduction in computing time compared to a
projected nested design approach.
