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

Social behaviors are crucial to improve the acceptance of a robot in human-shared environments. One of themost important social cues is undoubtedly the social space. This human mechanism acts like a repulsive field to guaranteecomfortable interactions. Its modeling has been widely studied in social robotics, but its experimental inference has beenweakly mentioned. Thereby, this paper proposes a novel algorithm to infer the dimensions of an elliptical social zone froma points-cloud around the robot. The approach consists of identifying how the humans avoid a robot during navigationin shared scenarios, and later use this experience to represent humans obstacles like elliptical potential fields with thepreviously identified dimensions. Thus, the algorithm starts with a first-learning stage where the robot navigates withoutavoiding 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 define the human-presence zonesaround 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 collisionavoidance. Finally, the performance of the learning algorithm and the motion control is verified through experimentation.