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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/20/8588 |
| _version_ | 1827719878571720704 |
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| author | Fumiko Ohori Hirozumi Yamaguchi Satoko Itaya Takeshi Matsumura |
| author_facet | Fumiko Ohori Hirozumi Yamaguchi Satoko Itaya Takeshi Matsumura |
| author_sort | Fumiko Ohori |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:53:54Z |
| format | Article |
| id | doaj.art-9fc6ba1865194a54b645a26918b0ab87 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:53:54Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9fc6ba1865194a54b645a26918b0ab872023-11-19T18:05:12ZengMDPI AGSensors1424-82202023-10-012320858810.3390/s23208588A Machine-Learning-Based Access Point Selection Strategy for Automated Guided Vehicles in Smart FactoriesFumiko Ohori0Hirozumi Yamaguchi1Satoko Itaya2Takeshi Matsumura3National Institute of Information and Communications Technology, Yokosuka 239-0847, JapanGraduate School of Information Science & Technology, Osaka University, Suita 565-0871, JapanNational Institute of Information and Communications Technology, Yokosuka 239-0847, JapanNational Institute of Information and Communications Technology, Yokosuka 239-0847, JapanAutomated 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.https://www.mdpi.com/1424-8220/23/20/8588flexible factoryproduction logisticsautomated guided vehicleradio channel measurementreceived signal strength indicatorlink quality estimation |
| spellingShingle | Fumiko Ohori Hirozumi Yamaguchi Satoko Itaya Takeshi Matsumura A Machine-Learning-Based Access Point Selection Strategy for Automated Guided Vehicles in Smart Factories Sensors flexible factory production logistics automated guided vehicle radio channel measurement received signal strength indicator link quality estimation |
| title | A Machine-Learning-Based Access Point Selection Strategy for Automated Guided Vehicles in Smart Factories |
| title_full | A Machine-Learning-Based Access Point Selection Strategy for Automated Guided Vehicles in Smart Factories |
| title_fullStr | A Machine-Learning-Based Access Point Selection Strategy for Automated Guided Vehicles in Smart Factories |
| title_full_unstemmed | A Machine-Learning-Based Access Point Selection Strategy for Automated Guided Vehicles in Smart Factories |
| title_short | A Machine-Learning-Based Access Point Selection Strategy for Automated Guided Vehicles in Smart Factories |
| title_sort | machine learning based access point selection strategy for automated guided vehicles in smart factories |
| topic | flexible factory production logistics automated guided vehicle radio channel measurement received signal strength indicator link quality estimation |
| url | https://www.mdpi.com/1424-8220/23/20/8588 |
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