A CPG-based gait planning method for bipedal robots

Abstract

Gait planning is one of the main focuses in the research field of bipedal robotics. To enhance the stability and simplicity of gait planning for bipedal robots using central pattern generator (CPG) methods, this paper first refines the existing Kimura oscillator model. Subsequently, an improved oscillator model is employed to propose a novel configuration of CPG network for flat walking gait planning in bipedal robots. A particle swarm algorithm with variable structural parameters is utilized to optimize the parameters of the CPG network, with the optimization objective being the maximization of stability margin at zero moment points (ZMP) during the walking process of the bipedal robot. Finally, an ADAMS simulation experiment platform is established to validate the feasibility of this method through simulation experiments. The experimental results indicate that this approach enables bipedal robots to achieve stable walking motion on a flat surface.