Cognitive social zones for improving the pedestrian collision avoidance with mobile robots

MATÍAS MONLLOR; DANIEL HERRERA; FLAVIO ROBERTI; JAVIER GIMÉNEZ; RICARDO CARELLI

Congreso; 2018 Congreso Latinoamericano de Control Automático; 2018

Social behaviors are crucial to improve the acceptance of a robot in human-shared environments. One of the most important social cues is undoubtedly the social space. This human mechanism acts like a repulsive eld to guarantee comfortable interactions. Its modeling has been widely studied in social robotics, but its experimental inference has been weakly mentioned. Thereby, this paper proposes a novel algorithm to infer the dimensions of an ellipticalsocial zone from a points-cloud around the robot. The approach consists of identifying how the humans avoid a robot during navigation in shared scenarios, and later use this experience to represent humans obstacles like elliptical potential elds with the previously identied dimensions. Thus, the algorithm starts with a rst-learning stage where the robot navigates without avoiding humans, i.e. the humans are in charge of avoiding the robots while developing their tasks. During this period, the robot generates a points-cloud with 2D laser measures from its own framework to dene the human-presence zones around itself but prioritizing its closest surroundings. Later, the inferred social zone is incorporated to a null-space-based (NSB) control for a non-holonomic mobile robot, which consists of both trajectory tracking and pedestrian collision avoidance. Finally, the performance of the learning algorithm and the motion control is veried through experimentation.