Person-Following Controller with Socially Acceptable Robot Motion

Abstract

This work presents a novel stable controller for the person-following task that includes social considerations for a differential drive mobile robot equipped with an RGB-D camera and a laser range finder as main sensors. The proposed controller adapts its behavior based on the knowledge of both: a modified personal space distribution and human user velocity. Control objectives are focused hence on keeping the human user within the camera's field-of-view while the mobile robot follows it, with a socially acceptable motion through arbitrary paths. To show the good behavior of this proposal, simulation and real experimental results are included and discussed. The asymptotic stability of the overall system is proved through the Lyapunov theory. Also, in our proposal, three state-of-the-art algorithms were integrated with the controller. In particular, a new real-time multi-person skeletal tracking system is used to obtain the relative human–robot position, a text to speech algorithm is used to confirm the commands given by the human, and also, a SLAM algorithm is used to obtain the map of the environment while the main task is being performed. Additionally, a hand gesture recognition module is included to interact with the mobile robot. This way, the robot is allowed to navigate with a socially-aware behavior in environments shared with humans. Finally, subjective and objective metrics are used as a validation method for human perception about the achieved robot motion.