Discrete time model predictive control for humanoid walking with step adjustment

TL;DR

This paper introduces a discrete-time MPC for humanoid walking that dynamically adjusts footsteps using a hierarchical control scheme, improving stability and fall prevention without predefined foot plans.

Contribution

The paper proposes a novel hierarchical MPC approach for humanoid walking that does not depend on predefined foot trajectories, enabling adaptive step adjustment and enhanced stability.

Findings

Successfully maintains humanoid stability in simulation

Prevents falls against push disturbances

Generates stable walking without predefined foot plans

Abstract

This paper presents a Discrete-Time Model Predictive Controller (MPC) for humanoid walking with online footstep adjustment. The proposed controller utilizes a hierarchical control approach. The high-level controller uses a low-dimensional Linear Inverted Pendulum Model (LIPM) to determine desired foot placement and Center of Mass (CoM) motion, to prevent falls while maintaining the desired velocity. A Task Space Controller (TSC) then tracks the desired motion obtained from the high-level controller, exploiting the whole-body dynamics of the humanoid. Our approach differs from existing MPC methods for walking pattern generation by not relying on a predefined foot-plan or a reference center of pressure (CoP) trajectory. The overall approach is tested in simulation on a torque-controlled Humanoid Robot. Results show that proposed control approach generates stable walking and prevents fall against push disturbances.