CPG-Based Control Scheme for Quadruped Robot to Withstand the Lateral Impact

TL;DR

This paper introduces a stability control strategy for quadruped robots under lateral impact, utilizing an extended CPG network and ZMP theory to improve impact resistance and maintain balance.

Contribution

The paper develops an extended four-neuron CPG network with trigger neurons for lateral trot control and integrates ZMP-based CoG planning to enhance robot stability during lateral impacts.

Findings

Lateral impact resistance increased by approximately 125%.

The robot's lateral acceleration recovers quickly after impact.

The proposed control scheme effectively maintains balance under lateral impacts.

Abstract

This paper aims to present a stability control strategy for quadruped robot under lateral impact with the help of lateral trot. We firstly propose five necessary conditions for keeping balance. The classical four-neuron Central Pattern Generator (CPG) network with Hopf oscillators is then extended to eight-neuron network with four more trigger-enabled neurons, which controls the lateral trot. With proper adjustment of network's parameters, such network can coordinate the lateral and longitudinal trot gait. Based on Zero Movement Point (ZMP) theory, the robot is modeled as an inverted pendulum to plan the Center of Gravity (CoG) position and calculate the needed lateral step length. The simulation shows that the lateral acceleration of the quadruped robot after lateral impact regains to the normal range in a short time. Comparison shows that the maximal lateral impact that robot can resist increases about 125% from 0.72g to 1.55g.