  This paper presents a Genetic Algorithm (GA) designed to reconfigure a large
group of modular Unmanned Aerial Vehicles (UAVs), each with different weights
and inertia parameters, into an over-actuated flight structure with improved
dynamic properties. Previous research efforts either utilized expert knowledge
to design flight structures for a specific task or relied on enumeration-based
algorithms that required extensive computation to find an optimal one. However,
both approaches encounter challenges in accommodating the heterogeneity among
modules. Our GA addresses these challenges by incorporating the complexities of
over-actuation and dynamic properties into its formulation. Additionally, we
employ a tree representation and a vector representation to describe flight
structures, facilitating efficient crossover operations and fitness evaluations
within the GA framework, respectively. Using cubic modular quadcopters capable
of functioning as omni-directional thrust generators, we validate that the
proposed approach can (i) adeptly identify suboptimal configurations ensuring
over-actuation while ensuring trajectory tracking accuracy and (ii)
significantly reduce computational costs compared to traditional
enumeration-based methods.
