We investigate the interaction between a pedestrian crowd and a social robot—a relevant human-robot interaction scenario observed in train stations and entrances to public places. We use a popular agent-based social force model to simulate a pedestrian crowd as it passes near a stationary robot. The modelling framework permits analysis of difficult-to-test situations such as high density crowds and the effects of contagion whereby more people are likely to interact with the robot if it is already surrounded by an audience. Accordingly, we augment the modelling framework to account for varying crowd densities, human-robot interaction, and social influence, and inform the parameter values from empirical studies in literature. Our results show that while the rate-of-interaction, defined as the number of agents located within an interacting distance per minute, increases with flow density, the average interaction-time is independent of the same. We find that inducing social influence through contagion does not have a significant effect on the rate at which agents engage with the robot in dense crowd scenarios, and has a marginal effect at low densities. The interaction-time was found to depend on the interaction-speed at which agents engage with the robot. These results indicate that at normally witnessed flow densities crowding near the robot is unlikely to take place, and the effect of robot design choices supersede the effects of social force or contagion.
['Sachit Butail']
Simulating the effect of a social robot on moving pedestrian crowds