Evolving Drone Morphologies for Agile Flight
Evolving Drone Morphologies for Agile Flight
This paper revisits rotary drone design by using an evolutionary algorithm to generate morphologies that outperform standard configurations in thrust-to-weight ratio, maneuverability, and size. The evolved drones show Pareto front extrema improvements of 489.2%, 23%, and 4.8% respectively compared to a standard quadcopter. Real-world experiments confirm performance gains in a drone racing task.
The study uses evolutionary algorithms to design drone morphologies that outperform standard quadcopters in thrust-to-weight ratio, maneuverability, and size, with real-world validation in a drone racing task showing improvements in gates passed, flight speed, and lap times.
Source: arXiv Robotics — research abstracts · Read original article ↗
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rphologies that performs better than standard configurations at thrust-to-weight ratio, maneuverability and size. To generate a range of drones with performance trade-offs between them, the non-dominated sorting genetic algorithm II is used. The evolved drone bodies outperform a standard multicopters in at least one of the three objectives. The Pareto front extrema show improvement of 489.2%, 23% and 4.8% in maneuver
Open source S3
terms of number of gates passed,flight speed and lap times.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org