Models for Global Synchronization in CPG-based Locomotion

Abstract

Various forms of animal locomotion have been studied in the biological literature. Neuroscience research suggests the existence of central pattern generators (CPGs), neural networks that generate periodic signals for locomotion. We study simplified modular architectures based on CPGs for robotic applications, and show their global exponential stability using partial contraction analysis. The proposed architectures can reproduce periodic CPG signals for swimming or walking motion of various animals. They can be combined towards increasingly complex behaviors while preserving stability