Human-Aware Robot Navigation around Groups in Narrow Spaces

The technological advancement in the field of Robotics and related areas is leading to the increasing deployment of artificial agents not only in factories but also in offices, hospitals, airports and, more recently, also in homes. One of the problems that robots face when deployed in environments with people is how to perform the so-called human-aware navigation, i.e., how to move around the environment complying with people's social conventions so that the humans around them feel safe and comfortable. In this regard, the social conventions typically followed by humans include keeping a certain distance from each other and not invading the space shared by the people gathered in a group. This thesis aims to develop and evaluate a robotic navigation framework that generates paths satisfying these two constraints. To achieve this goal, we developed a social model to represent both single humans and groups. In particular regarding group formations, we designed a geometric model able to detect, classify, and represent them adequately in the space. We integrated this social model with a state-of-the-art global path planner, obtaining an overall human and group aware navigation framework. We evaluated the system through a test set reproducing the typical structures of human groups, and then compared our resulting framework with the used state-of-the-art planner in terms of both performance and sociability. The results of the simulations confirm that, with the developed geometric model, it is possible to identify the formations of people in the environment, and consequently generating group-aware trajectories which result in higher levels of sociability.