Abstract
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.
Abstract (translated)
滑移是存在于轮式移动机器人系统中非常常见的现象,它具有不良的后果,如浪费能量和妨碍系统稳定性。为了应对在滑移条件下移动机器人轨迹跟踪的挑战,我们提出了一个Hierarchical框架,该框架同时在线学习并适应跟踪控制器的增益。具体来说,一个强化学习(RL)模块用于自动调整一个横向预测控制器和一个纵向速度PID控制器的参数。实验表明,同时进行增益调整是必要的,并已经证明我们的在线框架比使用固定增益的最佳基线控制器表现更好。通过利用在线增益适应,我们的框架可以在移动机器人穿越各种地形时拒绝滑移并减少跟踪误差,实现稳健的跟踪性能。
URL
https://arxiv.org/abs/2301.13283
