Online Learning Based Mobile Robot Controller Adaptation for Slip Reduction

TL;DR

This paper presents an online reinforcement learning framework that adaptively tunes control gains in real-time to reduce slip and improve trajectory tracking stability of wheeled mobile robots on slippery terrains.

Contribution

It introduces a hierarchical online learning approach for simultaneous gain adaptation in multiple controllers, enhancing robustness against slip in mobile robots.

Findings

Online gain tuning outperforms fixed-gain controllers.

Framework effectively reduces slip-induced tracking errors.

Adaptive control maintains stability across various terrains.

Abstract

Slip is a very common phenomena present in wheeled mobile robotic systems. It has undesirable consequences such as wasting energy and impeding system stability. To tackle the challenge of mobile robot trajectory tracking under slippery conditions, we propose a hierarchical framework that learns and adapts gains of the tracking controllers simultaneously online. Concretely, a reinforcement learning (RL) module is used to auto-tune parameters in a lateral predictive controller and a longitudinal speed PID controller. Experiments show the necessity of simultaneous gain tuning, and have demonstrated that our online framework outperforms the best baseline controller using fixed gains. By utilizing online gain adaptation, our framework achieves robust tracking performance by rejecting slip and reducing tracking errors when the mobile robot travels through various terrains.