Stabilization control of biped locomotion robot based learning with GAs having self-adaptive mutation and recurrent neural networks
Stabilization control of biped locomotion robot based learning with GAs having self-adaptive mutation and recurrent neural networks
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DOI：
10.1109/robot.1997.620041
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发表时间：
1997-04
期刊：
影响因子：
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通讯作者：
T. Fukuda;Youichirou Komata;T. Arakawa
中科院分区：
文献类型：
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作者：
T. Fukuda;Youichirou Komata;T. Arakawa
本研究的目的是产生两足步行机器人的自然运动，如人类在各种环境中的行走。本文提出了一种两足步行机器人稳定运动生成的方法。我们在双足行走机器人的鞋底安装了8个力传感器，并应用该方法进行了控制。零力矩(ZMP)是机器人行走稳定性的一个重要指标。ZMP由机器人的配置决定。然而，有许多针对ZMP的配置。正因为如此，当我们使用ZMP作为稳定性指标时，必须在许多稳定性构型中选择最优的构型。那么这就是选择哪些配置的问题。在本文中，我们用递归神经网络解决这个问题。在单支撑和双支撑两种情况下，利用每个鞋底上的四个力传感器的值来计算ZMP的位置，在不使ZMP离开鞋底支撑区的情况下，利用递归神经网络来确定执行关节和角度。采用基于遗传算法的递归神经网络学习能力和自适应变异算子。在此基础上，试制了一台具有13个关节的双足行走机器人，验证了所计算的稳定运动轨迹可以成功应用于实际的双足行走。
The purpose of this research is to generate natural motion of the biped locomotion robot such as the walking of a human in various environments. In this paper, we propose a method of stable motion generation of a biped locomotion robot. We apply the control of this proposed method with eight force sensors at the soles of the biped locomotion robot. The zero moment point (ZMP) is a well known index of stability in walking robots. ZMP is determined by the configuration of the robots. However, there are many configurations against the ZMP. Because of that, when we use ZMP as the stabilization index, we must select the best configuration in many stability configurations. Then it is a problem of which configurations are selected. In this paper, we solve the problem with recurrent neural networks. In both the single support and double support periods, we calculate the position of ZMP by using values from four force sensors at each sole, and actuation joints and the angles can be determined by recurrent neural networks without ZMP moving out from the supporting area of sole. We employ the recurrent neural networks with genetic algorithms for learning capability and self-adaptive mutation operator. Further, we build a biped locomotion robot in trial, which has 13 joints and verified that the calculated stable motion trajectory can be successfully applied to the practical biped locomotion.