深層強化学習による特定人物を追従しながら目的地へ到達する移動ロボットの開発

抄録

This study aims to develop a mobile robot to follow a specific human while avoiding obstacles and reaching to destinations. The behavior models for robot navigation are obtained by deep reinforcement learning in simulated environments representing simplified real-world conditions. The mobile robot in this study assumes to equip an RGB-D camera as an external sensor. The trained behavior model is then implemented in the actual robot for real-world navigation. Detecting and tracking of a specific target person is performed by combination of YOLO and SORT algorithms. Additionally, the RTAB-Map, which enables online simultaneous localization and mapping, allows the relative navigation from the current position without prior mapping.