Humans and robots working together in an environment to enhance human performance is the aim of Industry 5.0. Although significant progress in outdoor positioning has been seen, indoor positioning remains a challenge. In this paper, we introduce a new research concept by exploiting the potential of indoor positioning for Industry 5.0. We use Wi-Fi Received Signal Strength Indicator (RSSI) with trilateration using cheap and easily available ESP32 Arduino boards for positioning as well as sending effective route signals to a human and a robot working in a simulated-indoor factory environment in real-time. We utilized machine learning models to detect safe closeness between two co-workers (a human subject and a robot). Experimental data and analysis show an average deviation of less than 1m from the actual distance while the targets are mobile or stationary.
@article{arxiv.2303.14738,
title = {Indoor Positioning using Wi-Fi and Machine Learning for Industry 5.0},
author = {Inoj Neupane and Belal Alsinglawi and Khaled Rabie},
journal= {arXiv preprint arXiv:2303.14738},
year = {2023}
}