A Comparison of Fuzzy Logic Spatial Relationship Methods for Human Robot Interaction

Abstract

As the science of robotics advances, robots are interacting with people more frequently. Robots are appearing in our houses and places of work acting as assistants in many capacities. One aspect of this interaction is determining spatial relationships between objects. People and robots simply can not communicate effectively without references to the physical world and how those objects relate to each other. In this research fuzzy logic is used to help determine the spatial relationships between objects as fuzzy logic lends itself to the inherent imprecision of spatial relationships. Objects are rarely absolutely in front of or to the right of another, especially when dealing with multiple objects. This research compares three methods of fuzzy logic, the angle aggregation method, the centroid method and the histogram of angles – composition method. First we use a robot to gather real world data on the geometries between objects, and then we adapt the fuzzy logic techniques for the geometry between objects from the robot's perspective which is then used on the generated robot data. Last we perform an in depth analysis comparing the three techniques with the human survey data to determine which may predict spatial relationships most accurately under these conditions as a human would. Previous research mainly focused on determining spatial relationships from an allocentric, or bird's eye view, where here we apply some of the same techniques to determine spatial relationships from an egocentric, or observer's point of view.