Quadratic Programming-Based Posture Manipulation and Thrust-vectoring for Agile Dynamic Walking on Narrow Pathways

TL;DR

This paper presents a simulation study of a quadruped robot using quadratic programming-based control with thrusters for stable narrow-path walking and push recovery, enhancing agility and stability.

Contribution

It introduces a novel control framework combining centroidal dynamics and QP optimization for thruster-assisted walking on narrow paths.

Findings

Successful simulation of narrow-path walking with thruster stabilization

Effective lateral push-recovery using thrusters

Demonstrates improved stability and agility in simulation

Abstract

There has been significant advancement in legged robot's agility where they can show impressive acrobatic maneuvers, such as parkour. These maneuvers rely heavily on posture manipulation. To expand the stability and locomotion plasticity, we use the multi-modal ability in our legged-aerial platform, the Husky Beta, to perform thruster-assisted walking. This robot has thrusters on each of its sagittal knee joints which can be used to stabilize its frontal dynamic as it walks. In this work, we perform a simulation study of quadruped narrow-path walking with Husky $\beta$, where the robot will utilize its thrusters to stably walk on a narrow path. The controller is designed based on a centroidal dynamics model with thruster and foot ground contact forces as inputs. These inputs are regulated using a QP solver to be used in a model predictive control framework. In addition to narrow-path walking, we also perform a lateral push-recovery simulation to study how the thrusters can be used to stabilize the frontal dynamics.