When robots move through social spaces (i.e., environments shared with people) such as museums and shopping centers, they must navigate in a safe and socially acceptable manner to facilitate their inclusion and adoption. Therefore, robots operating in such settings must be able not only to avoid colliding with nearby obstacles, but also to show socially accepted behaviors, e.g., by minimizing the disruption in the comfort zone of nearby people. While there are well known approaches for social robot navigation, they are mostly based on social force models, which suffer from local minima. Meanwhile, other robot navigation frameworks do not consider social aspects. In this paper, we present an online social robot navigation framework, which is capable of generating collision free and socially acceptable paths online in uncontrolled crowded environments. Our proposed framework employs a modified sampling-based planner together with a new social relevance validity checking strategy. To evaluate our approach, we have designed a simulated social space in which the Pepper robot can safely navigate in a socially accepted manner. We compare our approach with other two alternative solutions while measuring specific social navigation metrics.

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In order to observe spatial relationships in social humanrobot interactions, a field trial was carried out within the CosmoCaixa Science Museum in Barcelona. The follow me episodes studied showed that the space configurations formed by guide and visitors walking together did not always fit the robot social affordances and navigation requirements to perform the guidance successfully, thus additional communication prompts are considered to regulate effectively the walking together and follow me behaviors.

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