Virtual Border Teaching Using a Network Robot System

Virtual borders are employed to allow users the flexible and interactive definition of their mobile robots' workspaces and to ensure a socially aware navigation in human-centered environments. They have been successfully defined using methods from human-robot interaction where a user directly interacts with the robot. However, since we recently witness an emergence of network robot systems (NRS) enhancing the perceptual and interaction abilities of a robot, we investigate the effect of such a NRS on the teaching of virtual borders and answer the question if an intelligent environment can improve the teaching process of virtual borders. For this purpose, we propose an interaction method based on a NRS and laser pointer as interaction device. This interaction method comprises an architecture that integrates robots into intelligent environments with the purpose of supporting the teaching process in terms of interaction and feedback, the cooperation between stationary and mobile cameras to perceive laser spots and an algorithm allowing the extraction of virtual borders from multiple camera observations. Our experimental results acquired from 15 participants' performances show that our system is equally successful and accurate while featuring a significant lower teaching time and a higher user experience compared to an approach without support of a NRS.