Stabilization control of biped locomotion robot based learning with GAs having self-adaptive mutation and recurrent neural networks

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.