Model-based predictive bipedal walking stabilization

A well known strategy in bipedal locomotion to prevent falling in the presence of large disturbances is to modify drastically future motion. This is an important capability of a walking control system in order to bring humanoid robots from controlled laboratory conditions to real environment situations. This paper presents a predictive stabilization method which modifies planned center of mass and foot trajectories depending on the current state of the robot. It uses a nonlinear prediction model [1] and applies a conjugate gradient method to solve the resulting optimization problem in real-time. Furthermore, the method is integrated in the walking control system of our bipedal robot LOLA. Simulation results demonstrate the effectiveness and the advantages of the proposed method.