強化学習による実世界の魚型ロボット群の分散円形フォーメーション制御
Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning
未知の非線形ダイナミクスを持つ魚型ロボット群に対し、強化学習と認知一致性理論に基づく分散制御器を提案し、シミュレーションのみで訓練した制御器を実機へチューニングなしで転移して円形フォーメーション制御を実現した。
著者: Tianhao Zhang, Yueheng Li, Shuai Li, Qiwei Ye, Chen Wang, Guangming Xie
分類: cs.RO, cs.AI, cs.MA
原文アブストラクト
In this paper, the circle formation control problem is addressed for a group of cooperative underactuated fish-like robots involving unknown nonlinear dynamics and disturbances. Based on the reinforcement learning and cognitive consistency theory, we propose a decentralized controller without the knowledge of the dynamics of the fish-like robots. The proposed controller can be transferred from simulation to reality. It is only trained in our established simulation environment, and the trained controller can be deployed to real robots without any manual tuning. Simulation results confirm that the proposed model-free robust formation control method is scalable with respect to the group size of the robots and outperforms other representative RL algorithms. Several experiments in the real world verify the effectiveness of our RL-based approach for circle formation control.
