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首页> 外文期刊>IEEE Transactions on Systems, Man, and Cybernetics >Biologically Inspired Control System for 3-D Locomotion of a Humanoid Biped Robot
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Biologically Inspired Control System for 3-D Locomotion of a Humanoid Biped Robot

机译:生物启发的类人两足动物机器人3-D运动控制系统

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摘要

This paper proposes the control system for 3-D locomotion of a humanoid biped robot based on a biological approach. The muscular system in the human body and the neural oscillator for generating locomotion signals are adapted in this paper. We extend the neuro-locomotion system for modeling a multiple neuron system, where motoric neurons represent the muscular system and sensoric neurons represent the sensor system inside the human body. The output signals from coupled neurons representing the angle joint level are controlled by gain neurons that represent the energy burst for driving the joint in each motor. The direction and the length of step in robot locomotion can be adjusted by command neurons. In order to form the locomotion pattern, we apply multiobjective evolutionary computation to solve the multiobjective problem when optimizing synapse weights between the motoric neurons. We use recurrent neural network (RNN) for the stabilization system required for supporting locomotion. RNN generates a dynamic weight synapse value between the sensoric neuron and the motoric neuron. The effectiveness of our system is demonstrated in open dynamic engine computer simulation and in a real robot application that has 12 degrees of freedom (DoFs) in legs and four DoFs in hands.
机译:本文提出了一种基于生物方法的人形两足动物机器人3-D运动控制系统。本文对人体的肌肉系统和产生运动信号的神经振荡器进行了调整。我们扩展了神经运动系统,以对多神经元系统建模,其中运动神经元代表肌肉系统,感觉神经元代表人体内部的传感器系统。代表角关节水平的来自耦合神经元的输出信号由增益神经元控制,增益神经元代表用于驱动每个电机中的关节的能量猝发。机器人运动的方向和步长可以通过命令神经元进行调整。为了形成运动模式,在优化运动神经元之间的突触权重时,我们应用多目标进化计算来解决多目标问题。我们将递归神经网络(RNN)用于支持运动所需的稳定系统。 RNN在感觉神经元和运动神经元之间生成动态权重突触值。我们的系统的有效性在开放式动态发动机计算机仿真中以及在实际机器人应用中得到了证明,该机器人应用的腿部具有12个自由度(DoF),而手中则具有四个DoF。

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