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.

翻译：打滑是轮式移动机器人系统中非常常见的现象，具有浪费能量和破坏系统稳定性等不良后果。为应对移动机器人在打滑条件下的轨迹跟踪挑战，我们提出了一种分层框架，能够同时在线学习并自适应调整跟踪控制器的增益。具体而言，采用强化学习（RL）模块对横向预测控制器和纵向速度PID控制器的参数进行自动调优。实验结果表明了同时调优增益的必要性，并证明我们的在线框架优于使用固定增益的最佳基线控制器。通过利用在线增益自适应，该框架能够在移动机器人穿越不同地形时抑制打滑并降低跟踪误差，从而实现鲁棒的跟踪性能。