産業5.0に向けたWi-Fiと機械学習による屋内測位
Indoor Positioning using Wi-Fi and Machine Learning for Industry 5.0
安価なESP32ボードでWi-Fi RSSIと三辺測量を行い、機械学習で人間とロボットの接近を検知する屋内測位手法を提案した。
著者: Inoj Neupane, Belal Alsinglawi, Khaled Rabie
分類: cs.RO, cs.NI
原文アブストラクト
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.