Locomotion as a Spatial-temporal Phenomenon: Models of the Central Pattern Generator

Abstract

The development of new approaches and new architectures for locomotion control in legged robots is of high interest in the area of robotic and intelligent motion systems, especially when the solution is easy both to conceive and to implement.

This first lecture emphasizes analog neural processing structures to realize artificial locomotion in mechatronic devices. The main inspiration comes from the biological paradigm of the Central Pattern Generator (CPG), used to model the neural populations responsible for locomotion planning and control in animals. The approach presented here starts by considering locomotion by legs as a complex spatio-temporal non linear dynamical system, modelled referring to particular types of reaction-diffusion non linear partial differential equations. In the following lecture these Spatio-temporal phenomena are obtained implementing the whole mathematical model on a new Reaction-Diffusion Cellular Neural Network (RD-CNN) architecture. Wave-like solutions as well as patterns are obtained, able to induce and control locomotion in some prototypes of biologically inspired walking machines. The design of the CNN structure is subsequently realized by analog circuits; this gives the possibility to generate locomotion in real time and also to control the transition among several types of locomotion. The methodology presented is applied referring to the experimental prototype of an hexapod robot. In the last lecture the same approach will be shown to be able to realize locomotion generation and control in a number of different robotic structures, such as ring worm-like robots or lamprey-like robots.