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Slip is a very common phenomena present in wheeled mobile robotic systems. Ithas undesirable consequences such as wasting energy and impeding systemstability. To tackle the challenge of mobile robot trajectory tracking underslippery conditions, we propose a hierarchical framework that learns and adaptsgains of the tracking controllers simultaneously online. Concretely, areinforcement learning (RL) module is used to auto-tune parameters in a lateralpredictive controller and a longitudinal speed PID controller. Experiments showthe necessity of simultaneous gain tuning, and have demonstrated that ouronline framework outperforms the best baseline controller using fixed gains. Byutilizing online gain adaptation, our framework achieves robust trackingperformance by rejecting slip and reducing tracking errors when the mobilerobot travels through various terrains.

Online Learning Based Mobile Robot Controller Adaptation for Slip Reduction