An architecture for indoor location-aided services based on collaborative industrial robotic platforms

Abstract

An essential component in the intelligent wireless processing for the future industrial halls will be the data labelling with location information. The location information will facilitate not only the remote control and autonomy of the industrial robots and sensors, but it will also enable predictive control and maintenance, increased productivity, and increased workers' safety. The data labelling is typically a tedious and costly process when done manually or semi-automatically, and the fully automated data labelling has still to overcome several challenges that we describe in this paper. We propose a collaborative robotic architecture equipped with simultaneous localization and mapping as well as machine-learning-based algorithms. A scenario in an industrial setting is presented, in which data acquisition by robots, with various capabilities, can be used to enable location-based services for increased workers' safety and to offer timely tracking of mobile assets for an increased productivity. The robotic platform acquires data during the periods when the robots are not allocated to their main tasks. Besides, we demonstrate that the above mentioned robotic platform could benefit from machine learning, for example, the accurate estimation of positions and good adaption in different type of collected data sets.