Parameter synthesis of coupled nonlinear oscillators for CPG-based robotic locomotion

This paper presents a numerical method for parameter synthesis of a central pattern generator (CPG) network to acquire desired locomotor patterns. The CPG network is modeled as a chain of unidirectionally or bidirectionally coupled Hopf oscillators with a novel coupling scheme that eliminates the influence of afferent signals on amplitude of the oscillator. The method converts the related CPG parameters into dynamic systems that evolve as part of the CPG network dynamics. The frequency, amplitude, and phase relations of teaching signals can be encoded by the CPG network with the proposed learning rules. The ability of the method to learn instructed locomotor pattern is proven with simulations. Application of the proposed method to online gait synthesis of a robotic fish is also presented.