Flight Structure Optimization of Modular Reconfigurable UAVs

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 im- proved dynamic properties. Previous research efforts either uti- lized 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 hetero- geneity 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 both over-actuation and trajectory tracking accuracy and (ii) significantly reduce computational costs compared to traditional enumeration-based methods.