Lydia A. France

Using dynamic mode decomposition on motion capture recordings of hawks, this preprint models behavioural states such as flapping, turning, landing, and gliding through simple and interpretable modal structures that can be linearly combined to reproduce experimental flight observations.

Perching at speed is among the most demanding flight behaviours that birds perform(1,2) and is beyond the capability of most autonomous vehicles. Smaller birds may touch down by hovering(3-8), but larger birds typically swoop up to perch(1,2)-presumably because the adverse scaling of their power margin prohibits hovering(9) and because swooping upwards transfers kinetic to potential energy before collision(1,2,10). Perching demands precise control of velocity and pose(11-14), particularly in larger birds for which scale effects make collisions especially hazardous(6,15). However, whereas cruising behaviours such as migration and commuting typically minimize the cost of transport or time of flight(16), the optimization of such unsteady flight manoeuvres remains largely unexplored(7,17). Here we show that the swooping trajectories of perching Harris’ hawks (Parabuteo unicinctus) minimize neither time nor energy alone, but rather minimize the distance flown after stalling. By combining motion capture data from 1,576 flights with flight dynamics modelling, we find that the birds’ choice of where to transition from powered dive to unpowered climb minimizes the distance over which high lift coefficients are required. Time and energy are therefore invested to provide the control authority needed to glide safely to the perch, rather than being minimized directly as in technical implementations of autonomous perching under nonlinear feedback control(12) and deep reinforcement learning(18,19). Naive birds learn this behaviour on the fly, so our findings suggest a heuristic principle that could guide reinforcement learning of autonomous perching.