Step and Timing Adaptation during Online DCM Trajectory Generation for Robust Humanoid Walking with Double Support Phases

Abstract

In this paper, we present a robust DCM-based online trajectory generator with step timing adaptation using MPC in addition to manipulating the ground reaction forces by the DCM tracking controller. The proposed control framework utilizes three strategies to react to disturbances: timing adaptation, footstep position adjustment, and CoP modulation. Most state-of-the-art walking controllers only address some of these aspects, and especially the timing adaptation is often neglected in the presence of double support phases as the resulting optimization problem generally becomes nonlinear. We show that we can keep the fast disturbance rejection from the DCM tracking controller while adjusting the timing and location of the footsteps via MPC if the CoP-based ankle strategy is insufficient to maintain balance. This framework is particularly relevant for robots with active control of the CoP inside the support polygon by utilizing a combination of ankle and step strategy to take full advantage of the robot's capabilities in response to external disturbances. The method is validated in simulation on the robot kangaroo. It has lightweight, fully actuated legs and a sufficiently large contact area, making it highly suitable for a combined step time adaptation and contact force modulation approach.