仿飞松鼠设计可折叠机翼无人机，提升敏捷飞行能力。
A highly maneuverable flying squirrel drone with agility-improving foldable wings
- 通过机翼与螺旋桨协同控制，拓展机动加速度范围。
- 实测跟踪误差降低13.1%，显著优于传统无翼无人机。
- 适合需要高机动性的复杂环境飞行任务。
无人机等空中飞行器受限于推力能力，难以实现敏捷飞行，仅靠控制算法无法根本解决。受飞松鼠启发，本文提出一种配备可折叠机翼的高机动无人机。通过推力-机翼协同控制（TWCC）框架，协调传统螺旋桨系统与可折叠机翼，扩展了可控加速度范围，实现传统无翼无人机无法达到的瞬时垂直力。采用物理辅助循环神经网络（paRNN）建模机翼复杂气动特性，动态调节迎角（AOA）以匹配真实气动行为。合理展开机翼产生的额外气阻显著提升了飞行跟踪性能。模型基于真实飞行数据训练，融合平板气动原理。实验结果表明，该“飞松鼠”无人机在均方根误差（RMSE）上比传统无翼无人机提升13.1%。演示视频见：https://youtu.be/O8nrip18azY。
原文摘要 · Abstract (English)
Drones, like most airborne aerial vehicles, face inherent disadvantages in achieving agile flight due to their limited thrust capabilities. These physical constraints cannot be fully addressed through advancements in control algorithms alone. Drawing inspiration from the winged flying squirrel, this paper proposes a highly maneuverable drone equipped with agility-enhancing foldable wings. By leveraging collaborative control between the conventional propeller system and the foldable wings-coordinated through the Thrust-Wing Coordination Control (TWCC) framework-the controllable acceleration set is expanded, enabling the generation of abrupt vertical forces that are unachievable with traditional wingless drones. The complex aerodynamics of the foldable wings are modeled using a physics-assisted recurrent neural network (paRNN), which calibrates the angle of attack (AOA) to align with the real aerodynamic behavior of the wings. The additional air resistance generated by appropriately deploying these wings significantly improves the tracking performance of the proposed "flying squirrel" drone. The model is trained on real flight data and incorporates flat-plate aerodynamic principles. Experimental results demonstrate that the proposed flying squirrel drone achieves a 13.1% improvement in tracking performance, as measured by root mean square error (RMSE), compared to a conventional wingless drone. A demonstration video is available on YouTube: https://youtu.be/O8nrip18azY.
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