Modeling and Trajectory Optimization for Standing Long Jumping of a Quadruped with A Preloaded Elastic Prismatic Spine

TL;DR

This paper introduces a new elastic spine model and trajectory optimization method for quadruped robots, significantly enhancing standing jump performance by leveraging spinal compliance and energy-efficient spring parameters.

Contribution

It proposes an actively preloaded elastic prismatic spine model and a constrained optimization approach to improve jumping distance in quadruped robots.

Findings

Less stiff springs improve jump distance by enabling more motor power use.

Elastic parameters significantly influence energy efficiency and jump performance.

Optimized spring coefficients can be visualized to identify ideal parameter regions.

Abstract

This paper presents a novel methodology to model and optimize trajectories of a quadrupedal robot with spinal compliance to improve standing jump performance compared to quadrupeds with a rigid spine. We introduce an elastic model for a prismatic robotic spine that is actively preloaded and mechanically lock-enabled at initial and maximum length, and develop a constrained trajectory optimization method to co-optimize the elastic parameters and motion trajectories toward enhanced jumping distance. Results reveal that a less stiff spring is likely to facilitate jumping performance not as a direct propelling source but as a means to unleash more motor power for propelling by trading-off overall energy efficiency. We also visualize the impact of spring coefficients on the overall optimization routine from energetic perspectives to identify the suitable parameter region.