Teach and Repeat 导航：一种鲁棒控制方法
Teach and Repeat Navigation: A Robust Control Approach
AI总结：
本文针对滑移转向移动机器人提出了一种基于滑模控制的新颖 Teach and Repeat (T&R) 系统，有效处理传感器噪声等不确定性，理论证明其全局稳定性，并在室内外多地形实验中优于现有方法。
AI中文摘要：
机器人导航需要一个对环境变化具有鲁棒性且能在不同条件下有效运行的自主流水线。Teach and Repeat (T&R) 导航在具有挑战性的环境下进行的自主重复任务中表现出高性能，但 T&R 内的研究主要集中在运动规划而非运动控制上。在本文中，我们提出了一种新颖的 T&R 系统，该系统基于鲁棒运动控制技术，针对滑移转向移动机器人采用滑模控制，有效处理了在 T&R 任务中尤为显著的不确定性，其中传感器噪声、参数不确定性以及车轮与地形交互是常见挑战。我们首先在理论上证明，在考虑闭环系统不确定性的情况下，所提出的 T&R 系统具有全局稳定性和鲁棒性。当部署在 Clearpath Jackal 机器人上时，我们随后展示了该系统在覆盖不同地形的室内和室外环境中的全局稳定性，并在这些具有挑战性的环境中，在平均轨迹误差和稳定性方面优于先前的最先进方法。本文朝着确保安全保证的长期自主 T&R 导航迈出了重要一步。
英文摘要：
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.
