Evolutionary algorithm designs multirotor drone morphology, outperforming standard quadrotors in racing
What happened
Elijah H. W. Ang and other authors published a paper using an evolutionary algorithm to search for multirotor body morphologies suited to different objectives and constraints, and used the non-dominated sorting genetic algorithm (NSGA-II) to generate a set of bodies with performance trade-offs. The authors claim that, compared with a standard quadrotor, the Pareto front extremes improved maneuverability, thrust-to-weight ratio, and size by 489.2%, 23%, and 4.8%, respectively; the maneuverability improvement comes from motor tilt, and the thrust-to-weight ratio improvement comes from more propellers. In real-drone racing experiments, the evolved bodies outperformed the standard body in number of gates passed, flight speed, and lap time, and the authors accordingly claim that the performance improvements can transfer to real racing tasks.
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- arXiv cs.ROEvolutionary 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.
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