采用最优控制的数值模拟方法可以估计出猴子手臂在到达运动过程中的刚度分布

Y. Ueyama, E. Miyashita
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引用次数: 3

摘要

了解大脑如何限制自由的维度来控制身体,将有利于人形机器人的机器人工程。我们估计了一只雌性日本猴(Macaca fuscata)在手臂伸展运动中的关节刚度,并进行了数值模拟。估计刚度在运动开始和运动结束时较高,在运动中点时降低。采用二连杆六肌臂模型和近似最优反馈控制的数值模拟再现了这些特征模式。虽然手臂模型是一个具有多个自由度的冗余系统,但最优控制能够通过优化与任务相关的代价函数来解决冗余问题。我们认为大脑可能根据类似的最优控制法则来控制身体。
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A numerical simulation using optimal control can estimate stiffness profiles of a monkey arm during reaching movements
An understanding of how the brain constrains dimensions of freedom to control the body would be beneficial for the robotic engineering of a humanoid robot. We estimated joint stiffness in a female Japanese monkey (Macaca fuscata) during arm reaching movements and carried out a numerical simulation. The estimated stiffness was high at movement onset and movement end, and decreased at the mid-point of the movement. These characteristic patterns were reproduced by the numerical simulation using a 2-link 6-muscle arm model and an approximately optimal feedback control. Although the arm model was a redundant system with multiple dimensions of freedom, the optimal control was able to solve the redundancy problem by optimizing a task-relevant cost function. We suggest that the brain may control the body according to a similar optimal control law.
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