地図ベース深層強化学習によるロボットナビゲーション
Robot Navigation with Map-Based Deep Reinforcement Learning
自己中心的な局所占有地図からCNNで操舵行動を予測する深層強化学習モデルを訓練し、実機ロボットに転移して動的障害物回避ナビゲーションを実現した。
著者: Guangda Chen, Lifan Pan, Yu'an Chen, Pei Xu, Zhiqiang Wang, Peichen Wu, Jianmin Ji, Xiaoping Chen
分類: cs.RO
原文アブストラクト
This paper proposes an end-to-end deep reinforcement learning approach for mobile robot navigation with dynamic obstacles avoidance. Using experience collected in a simulation environment, a convolutional neural network (CNN) is trained to predict proper steering actions of a robot from its egocentric local occupancy maps, which accommodate various sensors and fusion algorithms. The trained neural network is then transferred and executed on a real-world mobile robot to guide its local path planning. The new approach is evaluated both qualitatively and quantitatively in simulation and real-world robot experiments. The results show that the map-based end-to-end navigation model is easy to be deployed to a robotic platform, robust to sensor noise and outperforms other existing DRL-based models in many indicators.
