  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.
