Periodic Motion Control by Modulating CPG Parameters Based on Time-Series Recognition
Periodic Motion Control by Modulating CPG Parameters Based on Time-Series Recognition
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DOI：
10.1007/11553090_91
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发表时间：
2005-09
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影响因子：
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通讯作者：
T. Kondo;Koji Ito
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文献类型：
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作者：
T. Kondo;Koji Ito
本文提出了一种受生物脑-运动系统启发的冗余度机械臂计算运动控制模型。该模型由两个处理层组成，称为“CPG”和“动态记忆”。与脊髓中的生物中枢模式发生器类似，CPG层在产生用于实现周期性运动的扭矩模式中起作用。相反，更高的大脑模型，即动态记忆层是由递归神经网络（RNN）实现的时间序列模式识别。通过将系统状态的时间序列与优化的CPG参数相关联，RNN可以通过根据感觉运动时间序列召回非常合适的CPG参数来预测性地调制产生的扭矩模式。
This paper proposes a computational motion control model of a redundant manipulator inspired by biological brain-motor systems. The proposed model consists of two processing layers dubbed “CPG” and “Dynamical memory”. Likewise biological central pattern generators in spinal cord, the CPG layer plays a role in generating torque patterns for realizing periodic motions. On the contrary, the higher brain model, i.e. the Dynamical memory layer is a time-series pattern discriminator implemented by a recurrent neural networks (RNN). By associating time-series of the system states with optimized CPG parameters, the RNN can predictively modulate the generating torque patterns by recalling well-suited CPG parameters according to the sensorimotor time-series.