一种配备敏捷性增强折叠翼的高机动飞鼠无人机
A highly maneuverable flying squirrel drone with agility-improving foldable wings
AI总结：
本文受飞鼠启发提出一种带折叠翼的高机动无人机，通过推力-机翼协同控制框架和物理辅助循环神经网络建模折叠翼气动特性，实验表明其跟踪性能较传统无翼无人机提升13.1%。
摘要：
与大多数空中飞行器一样，无人机由于推力能力有限，在实现敏捷飞行方面面临固有劣势。这些物理约束仅靠控制算法的进步无法完全解决。受有翼飞鼠的启发，本文提出了一种配备敏捷性增强折叠翼的高机动无人机。通过利用传统螺旋桨系统与折叠翼之间的协同控制——由推力-机翼协同控制（Thrust-Wing Coordination Control, TWCC）框架协调——可控加速度集合得到扩展，从而能够产生传统无翼无人机无法实现的突发垂直力。折叠翼的复杂空气动力学特性使用物理辅助循环神经网络（physics-assisted recurrent neural network, paRNN）进行建模，该网络校准攻角（angle of attack, AOA）以匹配机翼的真实气动行为。通过适当展开这些机翼所产生的额外空气阻力显著提升了所提出的“飞鼠”无人机的跟踪性能。该模型基于真实飞行数据进行训练，并融合了平板气动原理。实验结果表明，以均方根误差（root mean square error, RMSE）衡量，所提出的飞鼠无人机相比传统无翼无人机实现了13.1%的跟踪性能提升。演示视频可在YouTube上观看：https://youtu.be/O8nrip18azY。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
