Human-Like ZMP Generator and Walking Stabilizer Based on Divergent Component of Motion

摘要

To realize natural and fast bipedal walking, we mimic the ZMP trajectory from human walking data based on the concept of Divergent Component of Motion (DCM). This paper presents an omnidirectional gait generator that is lightweight and universal. In addition, we integrate DCM tracking and foot placement adjustment into our control framework to ensure a stable walking motion and push recovery. The method proposed was validated in RoboCup2018 AdultSize soccer competition, where our robot WALKER+ walked on grass field and kept balance after the push from other robots.