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低阻抗行走机器人。

Low impedance walking robots.

机构信息

Franklin W. Olin College of Engineering, 1735 Great Plain Avenue, Needham, Massachusetts, 02492-1245.

出版信息

Integr Comp Biol. 2002 Feb;42(1):174-81. doi: 10.1093/icb/42.1.174.

DOI:10.1093/icb/42.1.174
PMID:21708707
Abstract

For both historical and technological reasons, most robots, including those meant to mimic animals or operate in natural environments,3 use actuators and control systems that have high (stiff) mechanical impedance. By contrast, most animals exhibit low (soft) impedance. While a robot's stiff joints may be programmed to closely imitate the recorded motion of an animal's soft joints, any unexpected position disturbances will generate reactive forces and torques much higher for the robot than for the animal. The dual of this is also true: while an animal will react to a force disturbance by significantly yielding position, a typical robot will greatly resist.These differences cause three deleterious effects for high impedance robots. First, the higher forces may cause damage to the robot or to its environment (which is particularly important if that environment includes people). Second, the robot must acquire very precise information about its position relative to the environment so as to minimize its velocity upon impact. Third, many of the self-stabilizing effects of natural dynamics are "shorted out"4 by the robot's high impedance, so that stabilization requires more effort from the control system.Over the past 5 yr, our laboratory has designed a series of walking robots based on "Series-Elastic Actuators" and "Virtual Model Control." Using these two techniques, we have been able to build low-impedance walking robots that are both safe and robust, that operate blindly without any model of upcoming terrain, and that add minimal control effort in parallel to their self-stabilizing passive dynamics. We have discovered that it is possible to achieve surprisingly effective ambulation from rather simple mechanisms and control systems. After describing the historical and technological motivations for our approach, this paper gives an overview of our methods and shows some of the results we have obtained.

摘要

出于历史和技术原因,大多数机器人,包括那些旨在模仿动物或在自然环境中运行的机器人,都使用具有高(硬)机械阻抗的致动器和控制系统。相比之下,大多数动物表现出低(软)阻抗。虽然机器人的刚性关节可以被编程来紧密模仿动物软关节的记录运动,但任何意外的位置干扰都会产生比动物高得多的反应力和扭矩。这种情况的对偶也同样适用:虽然动物会通过显著屈服位置来对力干扰做出反应,但典型的机器人会强烈抵抗。这些差异对高阻抗机器人造成了三个有害影响。首先,更高的力可能会对机器人或其环境造成损坏(如果环境包括人,这一点尤为重要)。其次,机器人必须获得关于其相对于环境的位置的非常精确的信息,以便在撞击时最小化其速度。第三,自然动力学的许多自稳定效应被机器人的高阻抗“短路”,因此稳定需要控制系统付出更多的努力。

在过去的 5 年中,我们的实验室设计了一系列基于“串联弹性致动器”和“虚拟模型控制”的步行机器人。使用这两种技术,我们已经能够构建具有低阻抗的步行机器人,这些机器人既安全又坚固,能够在没有任何即将到来的地形模型的情况下盲目运行,并且在其自稳定的被动动力学中添加最小的控制努力。我们发现,从相当简单的机制和控制系统中实现惊人有效的步行是可能的。在描述了我们方法的历史和技术动机之后,本文概述了我们的方法,并展示了我们获得的一些结果。

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引用本文的文献

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: A Bio-Inspired Machine Learning Approach to Estimating Posture in Robots Driven by Compliant Tendons.一种受生物启发的机器学习方法,用于估计由柔顺肌腱驱动的机器人的姿态。
Front Neurorobot. 2021 Oct 11;15:679122. doi: 10.3389/fnbot.2021.679122. eCollection 2021.
2
Impedance Control for Robotic Rehabilitation: A Robust Markovian Approach.机器人康复的阻抗控制:一种稳健的马尔可夫方法。
Front Neurorobot. 2017 Aug 24;11:43. doi: 10.3389/fnbot.2017.00043. eCollection 2017.