A Machine-Learning-Based Access Point Selection Strategy for Automated Guided Vehicles in Smart Factories

Automated Guided Vehicles (AGVs) are becoming popular at many manufacturing facilities. To ensure mobility and flexibility, AGVs are often controlled by wireless communication, eliminating the constraints of physical cables. These AGVs require multiple Access Points (APs) to ensure uninterrupted cov...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-10, Vol.23 (20), p.8588
Main Authors: Ohori, Fumiko, Yamaguchi, Hirozumi, Itaya, Satoko, Matsumura, Takeshi
Format: Article
Language:English
Subjects:
automated guided vehicle
Automatic guided vehicles
Automation
Computer peripherals industry
Factories
flexible factory
Forecasts and trends
link quality estimation
Machine learning
Manufacturing
production logistics
radio channel measurement
received signal strength indicator
Wireless communications
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Summary:Automated Guided Vehicles (AGVs) are becoming popular at many manufacturing facilities. To ensure mobility and flexibility, AGVs are often controlled by wireless communication, eliminating the constraints of physical cables. These AGVs require multiple Access Points (APs) to ensure uninterrupted coverage across the site. As AGVs move, they need to switch between these APs seamlessly. A primary challenge is that the communication downtime during this link-switching process must be minimal for effective AGV monitoring and control. Current AP selection strategies based on observed Received Signal Strength Indicator (RSSI) often fail in manufacturing environments due to RSSI’s inherent instability. This paper introduces a new AP selection technique for AGVs navigating these sites. Our approach harnesses the distinct movement patterns of AGVs and uses machine learning techniques to learn location-, trajectory-, and orientation-specific RSSI from the APs. Real-world factory data from our unique dataset revealed that our method extends the potential communication duration per route by 1.34 times compared to the prevalent signal strength-based switching methods commonly implemented in current drivers provided by chipset vendors or open-source Wi-Fi drivers. These results indicate that the automatic evaluation and tuning of the wireless environment using the proposed method is beneficial in reducing the time and effort required to investigate the detailed propagation paths needed to adapt AGV to existing APs.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23208588