A Robust Closed-Loop Biped Locomotion Planner Based on Time Varying Model Predictive Control

TL;DR

This paper introduces a robust, optimal closed-loop biped locomotion planner based on Time-Varying Model Predictive Control, capable of handling disturbances and generating human-like walking trajectories for humanoid robots.

Contribution

The paper presents a novel TVMPC-based planner that considers state, input, and output constraints, including vertical COM motion and DCM adjustments for improved robustness.

Findings

Successfully generates stable walking trajectories in simulations.

Demonstrates robustness against severe disturbances.

Produces human-like, mostly stretched knee walking patterns.

Abstract

Developing robust locomotion for humanoid robots is a complex task due to the unstable nature of these robots and also to the unpredictability of the terrain. A robust locomotion planner is one of the fundamental components for generating stable biped locomotion. This paper presents an optimal closed-loop biped locomotion planner which can plan reference trajectories even in challenging conditions. The proposed planner is designed based on a Time-Varying Model Predictive Control~(TVMPC) scheme to be able to consider some constraints in the states, inputs and outputs of the system and also mixed input-output. Moreover, the proposed planner takes into account the vertical motion of the Center of Mass~(COM) to generate walking with mostly stretched knees which is more human-like. Additionally, the planner uses the concept of Divergent Component of Motion~(DCM) to modify the reference ZMP online to improve the withstanding level of the robot in the presence of severe disturbances. The performance and also the robustness of the proposed planner are validated by performing several simulations using~\mbox{MATLAB}. The simulation results show that the proposed planner is capable of generating the biped locomotion robustly.