A Bioinspired Gait Transition Model for a Hexapod Robot

Abstract

Inspired by the analysis of the ant locomotion observed by the high-speed camera, an ant-like gait transition model for the hexapod robot is proposed in this paper. The model which consists of the central neural system (CNS), neural network (NN), and central pattern generators (CPGs) can produce the rhythmic signals for different gaits and can realize the transition of these gait automatically and smoothly according to the change of terrain. The proposed model suggests the neural mechanisms of the ant gait transition and can improve the environmental adaptability of the hexapod robot. The numerical simulation and corresponding physical experiment are implemented in this paper to verify the proposed method.