Walking gait planning using central pattern generators for hexapod walking robot

Abstract

This paper presents walking gait planning using a Central Pattern Generator (CPG) for a hexapod walking robot. Our CPG network model is introduced based on the Matsuoka’s neural oscillators, which is known as a neural network that generates rhythmic movements. Different output waveform can be obtained by setting the parameters of the model differently. To do this task, the followings are done. First, a CPGs network based on six Matsuoka oscillators to control the hip joint angle of the hexapod walking robot is built. Second, a mapping function to establish the relation between knee joint angle, ankle joint and hip joint is designed. Third, three kinds of gaits such as walking gait with five leg support, quadruped support gait and tripod support gait are generated by simulation. Finally, gait transition is presented by replacing the connection weight matrix of the model. Simulation results show that CPG can transition different gaits smoothly.