AI improves the location of objects inside industrial environments
A new project will use deep learning algorithms to increase the technology's accuracy and performance
Indoor positioning technologies are one of the driving forces behind the digital transformation of the industrial sector. The ability to track objects, assets, and people accurately and cheaply could save resources, time, and money for companies in various sectors — from logistics to health care. Xavier Vilajosana, a professor in the Faculty of Computer Science, Multimedia and Telecommunications and leader of the Wireless Networks group (WINE) at the Internet Interdisciplinary Institute (IN3) at the Universitat Oberta de Catalunya), is coordinating the university's participation in a new European project, which is developing innovative solutions to improve location in indoor environments. DUNE uses deep learning techniques combined with distributed computing systems to take advantage of both cloud and edge computing. In other words, these are computing architectures that operate both on remote servers and near where the data are generated. The aim is to create a versatile system that uses the various technologies available and which can adapt to the different potential cases of use.
"There are numerous technological approaches today that attempt to exploit the characteristics of radio signals as a tool for obtaining the relative position between objects,” Vilajosana said. “This technological variety and the wide range of situations in which they can be used with highly diverse budgets and environments for application means that we need to develop a powerful framework for managing location data from different technologies in real time, which at the same time is able to adapt to multiple industrial needs and is economically appealing."