Self-stabilizing bipedal locomotion employing neural oscillators

Abstract

For attaining a stable and robust dynamic bipedal locomotion, we address an efficient and powerful alternative based on biologically inspired control framework employing neural oscillators. Neural oscillators can be used to generate sustained rhythmic signals, and show superior features for stabilizing bipedal locomotion particularly when coupled with virtual impedance components. By building a network of neural oscillators, we can enable humanoid robots to walk stably and exhibit robustness against unexpected disturbances. Specifically, in order to maintain stability, the neural oscillator plays an important role by controlling the trajectory of the COM in phase with the ZMP input. The effectiveness of the proposed control scheme is verified through simulations.