Neural coupled central pattern generator based smooth gait transition of a biomimetic hexapod robot

Abstract

In this paper, a novel Central Pattern Generator (CPG) network topology based locomotion control strategy for a smooth gait transition of a biomimetic hexapod robot is proposed. Some preliminaries and correlations have been discussed to provide more suitable CPG network topology for both gait patterns that adapt to different environments, both in terms of transient state time and amplitude overshoot. The design network structure is developed with bidirectional diffusive coupling topologies to obtain robustness and efficient gait transitions. The stability of the proposed network is proved using coupling analyses. In contrast to conventional methods in the CPG network, the proposed method provides remarkable results that could generate four typical hexapod gaits transitions under rapid transient-state and steady-state conditions depending on the frequency, amplitude, and phase relationships among neurons. In order to govern the swing and stance phases according to the proposed network, the leg trajectory generator is designed and an inverse kinematics module is added to compute the link angles of the legs. By applying the proposed locomotion control strategy, the hexapod robot is capable of performing stable and rapid walking gaits. The simulation and experimental results show the effectiveness of the proposed method. High motion ability with the proposed network topology is provided considering walking frequency, forward speed, gait transition time, transient-state time, and steady-state comparisons with the literature.