Multirotor performance optimization using genetic algorithm

Designing and implementing a multirotor imposes some challenges: limited flight time and take-off mass, motor/propeller matching and unsteady dynamics. In this paper, these challenges are addressed by multi-objective optimization of a multiro tor's operational parameters like flight velocity, flight altitude and motor/propeller rpm, and physical parameters like motor, battery and propeller geometry. New contributions are establishing a functional dependence of rotor thrust and power coefficients on design parameters, and incorporating aerodynamic effects within the optimization environment. Additionally, the rationality of optimization is enhanced by modeling physical parameters as discrete variables. The numerical results indicate that Genetic Algorithm reliably finds an optimum design, and improves flight time and maximum take-off mass by 35%.