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Optimization of avian perching manoeuvres

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 mar...

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Autores principales: KleinHeerenbrink, Marco, France, Lydia A., Brighton, Caroline H., Taylor, Graham K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259480/
https://www.ncbi.nlm.nih.gov/pubmed/35768508
http://dx.doi.org/10.1038/s41586-022-04861-4
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author KleinHeerenbrink, Marco
France, Lydia A.
Brighton, Caroline H.
Taylor, Graham K.
author_facet KleinHeerenbrink, Marco
France, Lydia A.
Brighton, Caroline H.
Taylor, Graham K.
author_sort KleinHeerenbrink, Marco
collection PubMed
description 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.
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spelling pubmed-92594802022-07-08 Optimization of avian perching manoeuvres KleinHeerenbrink, Marco France, Lydia A. Brighton, Caroline H. Taylor, Graham K. Nature Article 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. Nature Publishing Group UK 2022-06-29 2022 /pmc/articles/PMC9259480/ /pubmed/35768508 http://dx.doi.org/10.1038/s41586-022-04861-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
KleinHeerenbrink, Marco
France, Lydia A.
Brighton, Caroline H.
Taylor, Graham K.
Optimization of avian perching manoeuvres
title Optimization of avian perching manoeuvres
title_full Optimization of avian perching manoeuvres
title_fullStr Optimization of avian perching manoeuvres
title_full_unstemmed Optimization of avian perching manoeuvres
title_short Optimization of avian perching manoeuvres
title_sort optimization of avian perching manoeuvres
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259480/
https://www.ncbi.nlm.nih.gov/pubmed/35768508
http://dx.doi.org/10.1038/s41586-022-04861-4
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