  The collective performance or capacity of collaborative autonomous systems
such as a swarm of robots is jointly influenced by the morphology and the
behavior of individual systems in that collective. In that context, this paper
explores how morphology impacts the learned tactical behavior of unmanned
aerial/ground robots performing reconnaissance and search & rescue. This is
achieved by presenting a computationally efficient framework to solve this
otherwise challenging problem of jointly optimizing the morphology and tactical
behavior of swarm robots. Key novel developments to this end include the use of
physical talent metrics and modification of graph reinforcement learning
architectures to allow joint learning of the swarm tactical policy and the
talent metrics (search speed, flight range, and cruising speed) that constrain
mobility and object/victim search capabilities of the aerial robots executing
these tactics. Implementation of this co-design approach is supported by
advancements to an open-source Pybullet-based swarm simulator that allows the
use of variable aerial asset capabilities. The results of the co-design are
observed to outperform those of tactics learning with a fixed Pareto design,
when compared in terms of mission performance metrics. Significant differences
in morphology and learned behavior are also observed by comparing the baseline
design and the co-design outcomes.
