Human following with environmental sensing and mapping

This thesis is concerned with human following by a mobile robot that also needs to navigate in a socially compliant manner. The importance of this problem is due to the increasing usage of service robots in human-populated areas. This is a challenging task because the robot needs to consider proxemics while navigating. At the same time, it needs to keep the human in sight. In this thesis, this problem is studied from four aspects. First, an extensive evaluation of the previously proposed social navigation method, Social APF-RL, has been conducted to verify that the robot's navigation is socially compliant. Following, in order to better keep the human target within its field of view, a social robot head design has been realized based on a pan-tilt mechanism. Thirdly, human following in a socially compliant manner has been considered using the developed robot head, including body-head coordination while navigating. For this, a deep learning network based on reinforcement learning has been developed. Finally, recovery in case of human target loss is considered through human tracking and environmental sensing of doors using a specially trained YOLOv8 network. All the proposed methods are tested extensively in the Gazebo simulation environment.