Teach and Repeat Navigation: A Robust Control Approach

TL;DR

This paper introduces a robust teach-and-repeat navigation system for skid-steering robots using sliding-mode control, demonstrating improved stability and accuracy in diverse environments with uncertainties.

Contribution

It presents a novel T&R control approach based on sliding-mode control that handles uncertainties and guarantees global stability, advancing autonomous navigation robustness.

Findings

Outperforms previous methods in trajectory accuracy

Proves global stability under uncertainties

Effective in indoor and outdoor terrains

Abstract

Robot navigation requires an autonomy pipeline that is robust to environmental changes and effective in varying conditions. Teach and Repeat (T&R) navigation has shown high performance in autonomous repeated tasks under challenging circumstances, but research within T&R has predominantly focused on motion planning as opposed to motion control. In this paper, we propose a novel T&R system based on a robust motion control technique for a skid-steering mobile robot using sliding-mode control that effectively handles uncertainties that are particularly pronounced in the T&R task, where sensor noises, parametric uncertainties, and wheel-terrain interaction are common challenges. We first theoretically demonstrate that the proposed T&R system is globally stable and robust while considering the uncertainties of the closed-loop system. When deployed on a Clearpath Jackal robot, we then show the global stability of the proposed system in both indoor and outdoor environments covering different terrains, outperforming previous state-of-the-art methods in terms of mean average trajectory error and stability in these challenging environments. This paper makes an important step towards long-term autonomous T&R navigation with ensured safety guarantees.