State Estimation for Hybrid Locomotion of Driving-Stepping Quadrupeds

TL;DR

This paper introduces a Kalman Filter-based state estimation method for hybrid driving-stepping quadruped robots with non-steerable wheels, enabling versatile locomotion on various terrains.

Contribution

It formulates a novel Kalman Filter that integrates wheel-driven motion into state estimation, adaptable to existing quadruped control frameworks.

Findings

Effective state estimation in simulation and real-world tests

Enables hybrid locomotion with driven wheels

Minor modifications needed for existing control systems

Abstract

Fast and versatile locomotion can be achieved with wheeled quadruped robots that drive quickly on flat terrain, but are also able to overcome challenging terrain by adapting their body pose and by making steps. In this paper, we present a state estimation approach for four-legged robots with non-steerable wheels that enables hybrid driving-stepping locomotion capabilities. We formulate a Kalman Filter (KF) for state estimation that integrates driven wheels into the filter equations and estimates the robot state (position and velocity) as well as the contribution of driving with wheels to the above state. Our estimation approach allows us to use the control framework of the Mini Cheetah quadruped robot with minor modifications. We tested our approach on this robot that we augmented with actively driven wheels in simulation and in the real world. The experimental results are available at https://www.ais.uni-bonn.de/%7Ehosseini/se-dsq .