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松鼠在身体在空中翻滚时,将头部/眼睛锁定在一个固定点以实现安全着陆的动力学与控制。

On the dynamics and control of a squirrel locking its head/eyes toward a fixed spot for safe landing while its body is tumbling in air.

作者信息

Ma Tianqi, Zhang Tao, Ma Ou

机构信息

Department of Automation, Tsinghua University, Beijing, China.

Department of Aerospace and Engineering Mechanics, University of Cincinnati, Cincinnati, OH, United States.

出版信息

Front Robot AI. 2022 Nov 24;9:1030601. doi: 10.3389/frobt.2022.1030601. eCollection 2022.

Abstract

An arboreal mammal such as a squirrel can amazingly lock its head (and thus eyes) toward a fixed spot for safe landing while its body is tumbling in air after unexpectedly being thrown into air. Such an impressive ability of body motion control of squirrels has been shown in a recent YouTube video, which has amazed public with over 100 million views. In the video, a squirrel attracted to food crawled onto an ejection device and was unknowingly ejected into air by the device. During the resulting projectile flight, the squirrel managed to quickly turn its head (eyes) toward and then keeps staring at the landing spot until it safely landed on feet. Understanding the underline dynamics and how the squirrel does this behavior can inspire robotics researchers to develop bio-inspired control strategies for challenging robotic operations such as hopping/jumping robots operating in an unstructured environment. To study this problem, we implemented a 2D multibody dynamics model, which simulated the dynamic motion behavior of the main body segments of a squirrel in a vertical motion plane. The inevitable physical contact between the body segments is also modeled and simulated. Then, we introduced two motion control methods aiming at locking the body representing the head of the squirrel toward a globally fixed spot while the other body segments of the squirrel were undergoing a general 2D rotation and translation. One of the control methods is a conventional proportional-derivative (PD) controller, and the other is a reinforcement learning (RL)-based controller. Our simulation-based experiment shows that both controllers can achieve the intended control goal, quickly turning and then locking the head toward a globally fixed spot under any feasible initial motion conditions. In comparison, the RL-based method is more robust against random noise in sensor data and also more robust under unexpected initial conditions.

摘要

像松鼠这样的树栖哺乳动物,在意外被抛入空中后身体在空中翻滚时,能惊人地将头部(进而眼睛)锁定在一个固定点以实现安全着陆。松鼠这种令人印象深刻的身体运动控制能力在最近的一个YouTube视频中得到了展示,该视频的观看量超过1亿次,令公众惊叹不已。在视频中,一只被食物吸引的松鼠爬到了一个弹射装置上,不知不觉地被该装置弹射到空中。在随后的抛射飞行过程中,松鼠设法迅速将头部(眼睛)转向并一直盯着着陆点,直到安全落地。了解其中的潜在动力学原理以及松鼠是如何做出这种行为的,能够启发机器人研究人员开发受生物启发的控制策略,用于诸如在非结构化环境中运行的跳跃机器人等具有挑战性的机器人操作。为了研究这个问题,我们实现了一个二维多体动力学模型,该模型模拟了松鼠在垂直运动平面内主要身体部位的动态运动行为。身体部位之间不可避免的物理接触也进行了建模和模拟。然后,我们引入了两种运动控制方法,旨在将代表松鼠头部的身体锁定在一个全局固定点上,而松鼠的其他身体部位则进行一般的二维旋转和平移。其中一种控制方法是传统的比例 - 微分(PD)控制器,另一种是基于强化学习(RL)的控制器。我们基于模拟的实验表明,两种控制器都能实现预期的控制目标,在任何可行的初始运动条件下都能快速转动并将头部锁定在全局固定点上。相比之下,基于RL的方法对传感器数据中的随机噪声更具鲁棒性,在意外初始条件下也更稳健。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7693/9729943/58cded100453/frobt-09-1030601-g001.jpg

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