Variable Horizon MPC with Swing Foot Dynamics for Bipedal Walking Control
This addresses locomotion stability for bipedal robots, but it is incremental as it builds on existing MPC methods with specific adaptations.
The paper tackles bipedal walking control by introducing a two-level variable horizon MPC framework that stabilizes center of mass dynamics and incorporates swing foot dynamics, achieving robust walking patterns on the Bolt robot in simulations and real experiments with disturbances.
In this paper, we present a novel two-level variable Horizon Model Predictive Control (VH-MPC) framework for bipedal locomotion. In this framework, the higher level computes the landing location and timing (horizon length) of the swing foot to stabilize the unstable part of the center of mass (CoM) dynamics, using feedback from the CoM states. The lower level takes into account the swing foot dynamics and generates dynamically consistent trajectories for landing at the desired time as close as possible to the desired location. To do that, we use a simplified model of the robot dynamics projected in swing foot space that takes into account joint torque constraints as well as the friction cone constraints of the stance foot. We show the effectiveness of our proposed control framework by implementing robust walking patterns on our torque-controlled and open-source biped robot, Bolt. We report extensive simulations and real robot experiments in the presence of various disturbances and uncertainties.