  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.
