An Interpretable Data-Driven Model of the Flight Dynamics of Hawks

TL;DR

This paper introduces an interpretable, data-driven dynamic mode decomposition model that captures hawk flight behaviors from motion data, revealing underlying mechanisms and enabling realistic extrapolation of flight dynamics.

Contribution

It presents the first physics-free, data-driven model of bird flight dynamics using DMD, capturing multiple behavioral modes with interpretability and accuracy.

Findings

Model accurately reconstructs hawk flight dynamics

Identifies shared dynamic modes across individual hawks

Enables extrapolation of naturalistic flapping behavior

Abstract

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.