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Indoor Positioning using Wi-Fi and Machine Learning for Industry 5.0

2023/03/26 by Inoj Neupane, Neupane, Inoj, Belal Alsinglawi +3
Engineering · #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #IoT-based Smart Home Systems #Networking and Internet Architecture (cs.NI) #Radio Wave Propagation Studies #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2303.14738

openalex publication_date 2023/03/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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