Self-adapting humanoid locomotion using a neural oscillator network

Abstract

Stable and robust dynamic locomotion has been gaining increasing attention in humanoid research. This paper presents a neural oscillator network for the generation of periodic locomotion patterns adapting to changes in the slope of the terrain. Specifically, locomotion trajectories of individual limbs are predetermined in the trajectory generator as a periodic function of the gait. The phase of the periodic function is coordinated with the output of the neural oscillator network incorporating sensory signals detecting the state of the foot in contact with the unknown changing terrain. For stability to be maintained, the neural oscillator plays an important role by controlling the trajectory of the COM in phase with the trajectory of the ZMP. In order to verify the validity of the proposed scheme, we carry out simulations and experiments. A preliminary investigation has yielded promising results, indicating that it may be applied to humanoid locomotion through uneven and uncertain terrain.