Advancing a taxonomy for proxemics in robot social navigation

Deploying robots in human environments requires effective social robot navigation. This article focuses on proxemics, proposing a new taxonomy and suggesting future directions through an analysis of state-of-the-art studies and the identification of research gaps. The various factors that affect the dynamic properties of proxemics patterns in human-robot interaction are thoroughly explored. To establish a coherent proxemics framework, we identified and organized the key parameters and attributes that shape proxemics behavior. Building on this framework, we introduce a novel approach to define proxemics in robot navigation, emphasizing the significant attributes that influence its structure and size. This leads to the development of a new taxonomy that serves as a foundation for guiding future research and development. Our findings underscore the complexity of defining personal distance, revealing it as a complex, multi-dimensional challenge. Furthermore, we highlight the flexible and dynamic nature of personal zone boundaries, which should be adaptable to different contexts and circumstances. Additionally, we propose a new layer for implementing proxemics in the navigation of social robots.