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Nature:鸟类栖息策略的优化 俯冲减降速后飞行距离 自动驾

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发表于 2022-7-18 11:39:58 | 显示全部楼层 |阅读模式
本帖最后由 顾汉现 于 2022-7-18 12:21 编辑

Nature:鸟类栖息策略的优化 俯冲减降速后的飞行距离  自动驾驶

鸟类栖息动作的优化

领研网

2022/07/18

论文
论文标题:Optimization of avian perching manoeuvres
作者:Marco KleinHeerenbrink, Lydia A. France, Caroline H. Brighton & Graham K. Taylor
期刊:Nature
发表时间:2022/06/29
数字识别码:10.1038/s41586-022-04861-4
摘要:Perching at speed is among the most demanding flight behaviours that birds perform1,2 and is beyond the capability of most autonomous vehicles. Smaller birds may touch down by hovering3,4,5,6,7,8, but larger birds typically swoop up to perch1,2—presumably because the adverse scaling of their power margin prohibits hovering9 and because swooping upwards transfers kinetic to potential energy before collision1,2,10. Perching demands precise control of velocity and pose11,12,13,14, particularly in larger birds for which scale effects make collisions especially hazardous6,15. However, whereas cruising behaviours such as migration and commuting typically minimize the cost of transport or time of flight16, the optimization of such unsteady flight manoeuvres remains largely unexplored7,17. 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 control12 and deep reinforcement learning18,19. Naive birds learn this behaviour on the fly, so our findings suggest a heuristic principle that could guide reinforcement learning of autonomous perching.

所属学科:
动物学

(导读 阿金)高速栖息是鸟类飞行行为中要求最复杂的一个,也是大部分自动驾驶车辆需要借鉴的。本研究调查了栗翅鹰(学名:Parabuteo unicinctus,又名哈里斯鹰)从飞行到停落的俯冲轨迹,发现时间和精力均未单独最小化,而是最大限度地减少降速后的飞行距离。结合动作捕捉技术和飞行动态模型,研究人员发现鸟类俯冲到爬升的转换点可实现高升力系数距离最小化,时间和精力可用于控制滑行以便安全停落。该结果为提升自动驾驶中自主停止的强化学习提供新的指导思路。

文章标签
鸟类飞行行为
停落
栗翅鹰
俯冲轨迹
自动驾驶

https://www.nature.com/articles/s41586-022-04861-4

https://www.linkresearcher.com/t ... 9-ac6b-01c3b7eca4f3



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