Towards Online Socially Acceptable Robot Navigation

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.