猛禽飞行动力学的可解释数据驱动模型
An Interpretable Data-Driven Model of the Flight Dynamics of Hawks
AI总结：
本文提出了一种基于动态模式分解的数据驱动模型，用于解释猛禽飞行动力学，通过分析运动捕捉数据揭示飞行动作的可解释模式结构。
摘要：
尽管对鸟类飞行已有大量分析，目前尚不存在生成物理模型来描述飞行动力学。然而，各种飞行动作背后的机制对于理解如何实现灵活飞行至关重要。即使在简单的飞行中，也存在多个目标，这使得整体飞行机制的分析变得更加复杂。通过使用动态模式分解（DMD）数据驱动方法对猛禽的运动捕捉记录进行分析，我们表明，诸如拍打、转弯、着陆和滑翔等多重行为状态可以通过简单的、可解释的模式结构（即底层翼尾形状）进行建模，这些模式可以线性组合以重现实验飞行观测。此外，DMD模型可以用来外推自然的拍打飞行。飞行高度个体化，猛禽之间在风格上存在差异，但我们发现它们共享一组共同的动力学模式。DMD模型直接拟合数据，不同于传统基于物理原理构建的模型，这些模型通常难以在真实数据上测试，其假设在真实飞行中通常无效。DMD方法仅需三个参数即可描述拍打，以及一个额外的参数来整合转弯动作。DMD分析进一步表明，飞行的底层机制，类似于最简单的步行模型，显示出主导模式之间的参数耦合，这表明了运动的效率。
英文摘要：
Despite significant analysis of bird flight, generative physics models for flight dynamics do not currently exist. Yet the underlying mechanisms responsible for various flight manoeuvres are important for understanding how agile flight can be accomplished. Even in a simple flight, multiple objectives are at play, complicating analysis of the overall flight mechanism. Using the data-driven method of dynamic mode decomposition (DMD) on motion capture recordings of hawks, we show that multiple behavioral states such as flapping, turning, landing, and gliding, can be modeled by simple and interpretable modal structures (i.e. the underlying wing-tail shape) which can be linearly combined to reproduce the experimental flight observations. Moreover, the DMD model can be used to extrapolate naturalistic flapping. Flight is highly individual, with differences in style across the hawks, but we find they share a common set of dynamic modes. The DMD model is a direct fit to data, unlike traditional models constructed from physics principles which can rarely be tested on real data and whose assumptions are typically invalid in real flight. The DMD approach gives a highly accurate reconstruction of the flight dynamics with only three parameters needed to characterize flapping, and a fourth to integrate turning manoeuvres. The DMD analysis further shows that the underlying mechanism of flight, much like simplest walking models, displays a parametric coupling between dominant modes suggesting efficiency for locomotion.
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。
