環境に応じた行動モデル生成と切り替えに基づく移動ロボットの自律走行システム

抄録

The purpose of this study is to develop a mobile robot navigation system based on switching of action models according to characteristics of environments. Action models according to several environments are trained by deep reinforcement learning. For example, crowded environments such as train stations, open environments such as parks and narrow environments such as office buildings are assumed as environments for action models training. In the preliminary system, one floor of the university building is divided into two environments, and the action models are trained in each environment. The mobile robot travels using the action model corresponding to the current environment in Unity simulation.