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.

翻译：机器人导航需要一种能够应对环境变化并在多变条件下保持有效的自主管道系统。教导与重复（T&R）导航已在具有挑战性的环境下的自主重复任务中展现出高性能，但现有T&R研究主要集中于运动规划而非运动控制。本文提出一种基于滑模控制的鲁棒运动控制技术的新型T&R系统，该系统有效处理T&R任务中尤为显著的传感器噪声、参数不确定性及轮地交互等常见挑战。我们首先从理论上证明，在考虑闭环系统不确定性的条件下，所提出的T&R系统具有全局稳定性和鲁棒性。当部署于Clearpath Jackal机器人时，我们进一步证明该系统在涵盖不同地形的室内外环境中均具有全局稳定性，且在平均轨迹误差和稳定性方面优于现有最先进方法。本文为具有安全保障的长期自主T&R导航迈出了重要一步。