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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 morphologyand collective behavior. There is especially a lack of computational frameworksto concurrently search for the robot morphology and the hyper-parameters oftheir behavior model that jointly optimize the collective (team) performance.To address this gap, this paper proposes a new co-design framework. Here theexploding computational cost of an otherwise nested morphology/behaviorco-design is effectively alleviated through the novel concept of ``talent"metrics; while also allowing significantly better solutions compared to thetypically sub-optimal sequential morphology$\to$behavior design approach. Thisframework comprises four major steps: talent metrics selection, talent Paretoexploration (a multi-objective morphology optimization process), behavioroptimization, and morphology finalization. This co-design concept isdemonstrated by applying it to design UAVs that operate as a team to localizesignal sources, e.g., in victim search and hazard localization. Here, thecollective behavior is driven by a recently reported batch Bayesian searchalgorithm called Bayes-Swarm. Our case studies show that the outcome ofco-design provides significantly higher success rates in signal sourcelocalization compared to a baseline design, across a variety of signalenvironments and teams with 6 to 15 UAVs. Moreover, this co-design processprovides two orders of magnitude reduction in computing time compared to aprojected nested design approach.

Efficient Concurrent Design of the Morphology of Unmanned Aerial Systems and their Collective-Search Behavior