Continuous Jumping for Legged Robots on Stepping Stones via Trajectory Optimization and Model Predictive Control

TL;DR

This paper introduces a novel framework combining trajectory optimization and model predictive control to enable legged robots to perform continuous, robust jumps on uneven stepping stones, addressing challenges in agile dynamic locomotion.

Contribution

The paper presents a new integrated approach using nonlinear trajectory optimization and MPC for continuous jumping on uneven terrains, improving robustness and transition smoothness.

Findings

Successfully generated periodic jumping trajectories for various distances.

Achieved smooth and continuous jumps on uneven platforms with unknown height perturbations.

Demonstrated robustness to model uncertainties and platform irregularities.

Abstract

Performing highly agile dynamic motions, such as jumping or running on uneven stepping stones has remained a challenging problem in legged robot locomotion. This paper presents a framework that combines trajectory optimization and model predictive control to perform robust and consecutive jumping on stepping stones. In our approach, we first utilize trajectory optimization based on full-nonlinear dynamics of the robot to generate periodic jumping trajectories for various jumping distances. A jumping controller based on a model predictive control is then designed for realizing smooth jumping transitions, enabling the robot to achieve continuous jumps on stepping stones. Thanks to the incorporation of MPC as a real-time feedback controller, the proposed framework is also validated to be robust to uneven platforms with unknown height perturbations and model uncertainty on the robot dynamics.