Interactive person following for social robots

Abstract

In this study we propose an innovative decision making engine for interactive person following for a mobile social robot. The developed engine combines 1. A Strategy controller that selects the strategy to apply 2. A Multiple-Objective system that provides to the robot the way to perform this strategy. We show the performances of this system by two sets of experiments (in simulation environment): the first one is based only on the observed data; in the second one we predict the state of the user with a Kalman filter and the state of the robot with a predictive model. We demonstrate that the proposed reasoning system is able to regulate the robot behavior in complex and critical situations. The suggested predictors improve the system performances considerably.