A 3-Step Optimization Framework with Hybrid Models for a Humanoid Robot's Jump Motion

TL;DR

This paper introduces a three-step optimization framework combining models and quadratic programming to generate high-dynamic jump motions for humanoid robots, validated through simulations and experiments.

Contribution

It presents a novel 3-step trajectory optimization framework that integrates momentum, inertia, and whole-body joint trajectories for humanoid robot jumps.

Findings

Successful simulation and experimental validation of 1.0 m forward jump

Framework achieves agile and high-dynamic jump motions

Improved iteration speed and performance in trajectory optimization

Abstract

High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper proposes a 3-step trajectory optimization framework for generating a jump motion for a humanoid robot. To improve iteration speed and achieve ideal performance, the framework comprises three sub-optimizations. The first optimization incorporates momentum, inertia, and center of pressure (CoP), treating the robot as a static reaction momentum pendulum (SRMP) model to generate corresponding trajectories. The second optimization maps these trajectories to joint space using effective Quadratic Programming (QP) solvers. Finally, the third optimization generates whole-body joint trajectories utilizing trajectories generated by previous parts. With the combined consideration of momentum and inertia, the robot achieves agile forward jump motions. A simulation and experiments (Fig. \ref{Fig First page fig}) of forward jump with a distance of 1.0 m and 0.5 m height are presented in this paper, validating the applicability of the proposed framework.