Two-phase GA parameter tunning method of CPGs for quadruped gaits

Nowadays, the locomotion control research field has been pretty active and has produced different approaches for legged robots. From biological studies, it is known that fundamental rhythmic periodical signals for locomotion are produced by Central Pattern Generator (CPG) and the main part of the coordination takes place in the central nervous system. In spite of the CPG-utility, there are few training methodologies to generate the rhythmic signals based in CPG models. In this paper, an automatic method to find the synaptic weights to generate three basic gaits using Genetic Algorithms (GA) is presented. The method is based on the analysis of the oscillator behavior and its interactions with other oscillators, in a network. The oscillator model used in this work is the proposed by Van Der Pol (VDP). A two-phase GA is adapted: (i) to find the parameter values to produce oscillations and (ii) to generate the weight values of the interconnections between oscillators. The results show the feasibility of the presented method to find the parameters to generate different gaits. The implementation takes advantage that the fitness function works directly with the oscillator and the network. So, knowledge about the robot dynamic is not necessary. The GA based approach uses small population and limited numbers of generations, ideal to be processed on either computers with reduced resources or hardware implementations.