Socially-accepted Path Planning for Robot Navigation based on Social Interaction Spaces

Abstract

Path planning is one of the most widely studied problems in robot navigation. It deals with estimating an optimal set of waypoints from an initial to a target coordinate. New generations of assistive robots should be able to compute these paths considering not only obstacles but also social conventions. This ability is commonly referred to as social navigation. This paper describes a new socially-acceptable path-planning framework where robots avoid entering areas corresponding to the personal spaces of people, but most importantly, areas related to human-human and human-object interaction. To estimate the social cost of invading personal spaces we use the concept of proxemics. To model the social cost of invading areas where interaction is happening we include the concept of object interaction space. The framework uses Dijkstra's algorithm on a uniform graph of free space where edges are weighed according to the social traversal cost of their outbound node. Experimental results demonstrate the validity of the proposal to plan socially-accepted paths.