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

• Main Authors: Daniel Herrera, Javier Gimenez, Matias Monllor, Flavio Roberti, Ricardo Carelli
• Format: Article
• Language: Spanish
• Published: Escuela Politécnica Nacional (EPN) 2019-01-01
• Series: Revista Politécnica
• Online Access: https://revistapolitecnica.epn.edu.ec/ojs2/index.php/revista_politecnica2/article/view/1015

Abstract: 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. DOI: https://doi.org/10.33333/rp.vol42n2.1015