A Series Inspired CPG Model for Robot Walking Control

Abstract

Central pattern generator (CPG) is a kind of neural network which is located in the spinal cord. It has been found to be responsible for many rhythmic biological movements, such as breathing, swimming, flying as well as walking. Many CPG models have been designed and proved to be useful. But the CPG outputs of these models are often sine waves or quasi-sine waves. Also these outputs are directly used as the control signals to control joint trajectories or joint torques on robots. This is obviously not an accurate design in robot walking control especially when sine or quasisine waves are not the best signals to set walking patters because of the complexity of tasks. In this paper, based on the idea of Righetti, Buchli and Ijspeert, a CPG model is designed, which is inspired by Fourier series and can produce outputs with any shape. There are a limited set of sub-components in the proposed model. Each sub-component learns one harmonic of a reference wave. A summation of these sub-components is used to approximate the wave. In this way, the wave will be learned and embedded in the CPG model. In the proposed model, FFT is used to see the harmonics and calculate the frequency. The system is designed in polar coordinates with new Hebbian learning items and Kuramoto model items. Because the whole system is a limit cycle system, it is robust to perturbation. The experiment conducted on an AIBO robot shows the effectiveness of the proposed model.