3LP: a linear 3D-walking model including torso and swing dynamics

TL;DR

This paper introduces a linear 3D walking model with torso and swing dynamics that enables fast, feasible gait prediction and control for humanoid robots, closely mimicking human torque and force profiles.

Contribution

The paper presents a novel linear biped locomotion model incorporating torso and swing dynamics, enabling closed-form gait solutions and improved control for humanoid robots.

Findings

Produces human-like torque and ground reaction force profiles

Predicts nonlinear speed-frequency relationship in human walking

Provides a fast, scalable platform for model predictive control

Abstract

In this paper, we present a new model of biped locomotion which is composed of three linear pendulums (one per leg and one for the whole upper body) to describe stance, swing and torso dynamics. In addition to double support, this model has different actuation possibilities in the swing hip and stance ankle which could be widely used to produce different walking gaits. Without the need for numerical time-integration, closed-form solutions help finding periodic gaits which could be simply scaled in certain dimensions to modulate the motion online. Thanks to linearity properties, the proposed model can provide a computationally fast platform for model predictive controllers to predict the future and consider meaningful inequality constraints to ensure feasibility of the motion. Such property is coming from describing dynamics with joint torques directly and therefore, reflecting hardware limitations more precisely, even in the very abstract high level template space. The proposed model produces human-like torque and ground reaction force profiles and thus, compared to point-mass models, it is more promising for precise control of humanoid robots. Despite being linear and lacking many other features of human walking like CoM excursion, knee flexion and ground clearance, we show that the proposed model can predict one of the main optimality trends in human walking, i.e. nonlinear speed-frequency relationship. In this paper, we mainly focus on describing the model and its capabilities, comparing it with human data and calculating optimal human gait variables. Setting up control problems and advanced biomechanical analysis still remain for future works.