オンライン学習による車輪移動ロボットのスリップ低減のための制御器適応
Online Learning Based Mobile Robot Controller Adaptation for Slip Reduction
強化学習で横方向予測制御器と縦方向PID制御器のゲインをオンライン同時調整し、滑りやすい路面でも軌道追従性能を向上させる階層フレームワークを提案した。
著者: Huidong Gao, Rui Zhou, Masayoshi Tomizuka, Zhuo Xu
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
