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Humanoid motion analysis and control based on COG viscoelasticity

机译:基于COG粘弹性的人形运动分析与控制

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摘要

This paper proposes a concept of center of gravity (COG) viscoelasticity to associate joint viscoelasticity with the inverted pendulum model of humanoid dynamics. Although COG viscoelasticity is based on the well-known kinematic relationship between joint stiffness and end-effector stiffness, it provides practical advantages for both analysis and control of humanoid motions. There are two main contributions. The first is that the COG viscoelasticity allows us to analyze fall risk. In a previous study, the author proposed a fall detection method based on the maximal output admissible (MOA) set, which is computed from feedback gain of the inverted pendulum model. The COG viscoelasticity associates joint viscoelasticity with the feedback gain and allows us to compute the corresponding MOA set when an arbitrary joint viscoelasticity is given. The second contribution is that the COG viscoelasticity can be also utilized in motion control. After we design a feedback gain in the inverted pendulum model utilizing the control theory, the COG viscoelasticity can directly transform it to the joint viscoelasticity. The validity of the COG viscoelasticity is verified with whole-body dynamics simulations.
机译:本文提出了重心(COG)粘弹性的概念,将关节粘弹性与人形动力学的倒摆模型联系起来。尽管COG粘弹性基于关节刚度和末端执行器刚度之间众所周知的运动学关系,但它为分析和控制人形运动提供了实际优势。有两个主要贡献。首先,COG粘弹性使我们能够分析跌倒风险。在之前的一项研究中,作者提出了一种基于最大输出允许(MOA)集的跌倒检测方法,该方法由倒摆模型的反馈增益计算得出。COG 粘弹性将关节粘弹性与反馈增益相关联,并允许我们在给定任意关节粘弹性时计算相应的 MOA 集。第二个贡献是COG粘弹性也可用于运动控制。利用控制理论在倒摆模型中设计反馈增益后,COG黏弹性可以直接转化为关节粘弹性。通过全身动力学模拟验证了COG粘弹性的有效性。

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