← 研踪 PaperTrailarXiv:2502.01197 · v2
Evolving Drone Morphologies for Agile Flight
首次提交 2025-02-03 17:40
版本更新 2026-11-06 20:32
雷达观测 2026-11-09 11:19
摘要
The design of multicopter drones has remained almost the same since their inception. While conventional designs work well in many cases, they may not be optimal in specific tasks. This paper revisits rotary drone design by exploring which body morphologies are best suited for different objectives and constraints. An evolutionary algorithm is used to produce drone morphologies 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 maneuverability, thrust-to-weight ratio and size, respectively, when compared to a standard quadcopter. The improvement in maneuverability is attributed to the tilt angles of the motors, while the increase in thrust-to-weight ratio is due to the higher number of propellers. Experiments show that the performance improvement transfers to a real world drone racing task, where the evolved drone outperforms the standard drone in terms of number of gates passed,flight speed and lap times.
作者备注：29 pages, 12 figures
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v2 · 2026-11-06 20:32 · 最新观测版本
Evolving Drone Morphologies for Agile Flight
The design of multicopter drones has remained almost the same since their inception. While conventional designs work well in many cases, they may not be optimal in specific tasks. This paper revisits rotary drone design by exploring which body morphologies are best suited for different objectives and constraints. An evolutionary algorithm is used to produce drone morphologies 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 maneuverability, thrust-to-weight ratio and size, respectively, when compared to a standard quadcopter. The improvement in maneuverability is attributed to the tilt angles of the motors, while the increase in thrust-to-weight ratio is due to the higher number of propellers. Experiments show that the performance improvement transfers to a real world drone racing task, where the evolved drone outperforms the standard drone in terms of number of gates passed,flight speed and lap times.
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