Affordance-map: A map for context-aware path planning

Abstract

'Context-awareness' could be one of the most desired fundamental abilities that a robot should have when sharing a workspace with humans co-workers. Arguably, a robot with appropriate context-awareness could lead to a better human robot interaction. In this paper, we address the problem of combining contextawareness with robotic path planning. Our approach is based on affordance-map, which involves mapping latent human actions in a given environment by looking at geometric features of the environment. This enables us to learn human context in an given environment without observing real human behaviours which themselves are a non-trivial task to detect. Once learned, affordance-map allows us to assign an affordance cost value for each grid location of the map. These cost maps are later used to develop a context-aware global path planning strategy by using the well known A∗ algorithm. The proposed method was tested in a real office environment and proved our algorithm is capable of moving a robot in a path that minimises the distractions to human co-workers.