用进化算法设计更优无人机形态，兼顾推重比、机动性和体积。
Multi-objective Evolution of Drone Morphology
- 通过NSGA-II算法演化多种无人机形态，优化三类性能目标。
- 最优形态在机动性上提升487.8%，推重比提升23.5%，体积缩小4.8%。
- 适合需要定制化无人机设计的科研与工程人员参考。
多旋翼无人机的设计自问世以来几乎未变。尽管传统设计如四旋翼在多数场景下表现良好，但在特定环境或任务中未必最优。本文重新审视旋翼无人机设计，探索不同目标与约束下的最优机体形态。具体而言，采用进化算法生成三种目标的最优无人机形态：（1）高推重比，（2）高机动性，（3）小尺寸。为获得性能权衡的多样化解，使用非支配排序遗传算法II（NSGA-II），随机初始化600个个体，演化2000代。结果表明，所获形态在至少一项指标上优于标准5英寸、220毫米轮距四旋翼。帕累托前沿三极端解分别实现机动性提升487.8%、推重比提升23.5%、尺寸缩小4.8%。机动性提升源于螺旋桨倾角优化，推重比提升主要归因于更多螺旋桨配置。四旋翼位于帕累托前沿，但其他设计在特定目标下表现更优。
原文摘要 · Abstract (English)
The design of multicopter drones has remained almost the same since its inception. While conventional designs, such as the quadcopter, work well in many cases, they may not be optimal in specific environments or missions. This paper revisits rotary drone design by exploring which body morphologies are optimal for different objectives and constraints. Specifically, an evolutionary algorithm is used to produce optimal drone morphologies for three objectives: (1) high thrust-to-weight ratio, (2) high maneuverability, and (3) small size. To generate a range of optimal drones with performance trade-offs between them, the non-dominated sorting genetic algorithm II, or NSGA-II is used. A randomly sampled population of 600 is evolved over 2000 generations. The NSGA-II algorithm evolved drone bodies that outperform a standard 5-inch 220 mm wheelbase quadcopter in at least one of the three objectives. The three extrema in the Pareto front show improvement of 487.8%, 23.5% and 4.8% in maneuverability, thrust-to-weight ratio and size, respectively. The improvement in maneuverability can be attributed to the tilt angles of the propellers, while the increase in thrust-to-weight ratio is primarily due to the higher number of propellers. The quadcopter is located on the Pareto front for the three objectives. However, our results also show that other designs can be better depending on the objectives.
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