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A Recurrent Neural Network Model for Lamprey-Like Robot Movement

机译:类似于Lamprey的机器人运动的递归神经网络模型

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One of the most important problems in controlling robot locomotion, is how to generate an online trajectory for robots with a large number of degrees of freedom. In this paper, we present a biomimetic approach which is based on Central Pattern Generator (CPG) inspired from the lamprey to solve this problem. CPG is a neural network found in animal and has the ability of producing rhythmic pattern without receiving rhythmic input. We used a recurrent neural network(RNN) to model the CPG and proposed the genetic algorithm to train the RNN to follow a predefined oscillatory pattern. It is furthermore demonstrated that recurrent neural networks do indeed exhibit oscillatory behavior and may in this way be used to imitate the function of the CPG responsible for locomotion in animate creatures.
机译:控制机器人运动的最重要问题之一是如何为具有大量自由度的机器人生成在线轨迹。在本文中,我们提出了一种仿生方法,该方法基于受七the鳗启发的中央模式生成器(CPG)来解决此问题。 CPG是在动物中发现的神经网络,具有在不接收节奏输入的情况下产生节奏模式的能力。我们使用递归神经网络(RNN)对CPG进行建模,并提出了遗传算法来训练RNN遵循预定义的振荡模式。进一步证明,递归神经网络确实确实表现出振荡行为,并且可以这种方式被用来模仿负责动画动物中运动的CPG的功能。

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