Optimization of avian perching manoeuvres

被引:24
作者
KleinHeerenbrink, Marco [1 ]
France, Lydia A. [1 ,2 ]
Brighton, Caroline H. [1 ]
Taylor, Graham K. [1 ]
机构
[1] Univ Oxford, Dept Zool, Oxford, England
[2] Alan Turing Inst, London, England
基金
英国生物技术与生命科学研究理事会; 欧洲研究理事会;
关键词
FLIGHT; AERODYNAMICS; MECHANICS; KINEMATICS; PARABUTEO; DYNAMICS; PIGEONS; BIRD; HAWK;
D O I
10.1038/s41586-022-04861-4
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:91 / +
页数:19
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