Evolutionary algorithm generates multicopter body morphologies with agile flight performance superior to standard quadcopters

The paper uses an evolutionary algorithm to search for multicopter body morphologies suited to different objectives and constraints, and uses the non-dominated sorting genetic algorithm II (NSGA-II) to generate a set of bodies with performance trade-offs. Compared with a standard quadcopter, the Pareto front extrema improve maneuverability, thrust-to-weight ratio, and size by 489.2%, 23%, and 4.8%, respectively; the maneuverability improvement comes from motor tilt angles, and the thrust-to-weight ratio improvement comes from more propellers. Real drone racing experiments show that the evolved bodies outperform the standard body in number of gates passed, flight speed, and lap time.