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

Fumiko Ohori1,2, Hirozumi Yamaguchi2, Satoko Itaya1

  • 1National Institute of Information and Communications Technology, Yokosuka 239-0847, Japan.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
Summary

This study introduces a new machine learning method for Automated Guided Vehicles (AGVs) to improve wireless access point (AP) switching. The technique enhances communication duration by 1.34 times, reducing downtime in manufacturing environments.

Keywords:
automated guided vehicleflexible factorylink quality estimationmachine learningproduction logisticsradio channel measurementreceived signal strength indicator

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Area of Science:

  • Industrial Automation
  • Wireless Communication Systems
  • Machine Learning Applications

Background:

  • Automated Guided Vehicles (AGVs) are increasingly deployed in manufacturing for enhanced mobility and flexibility.
  • Wireless communication is crucial for AGV operation, necessitating seamless transitions between multiple Access Points (APs).
  • Existing Received Signal Strength Indicator (RSSI) based AP selection methods are unreliable in dynamic manufacturing environments due to signal instability.

Purpose of the Study:

  • To develop an advanced AP selection technique for AGVs operating in manufacturing settings.
  • To minimize communication downtime during AP switching for reliable AGV monitoring and control.
  • To overcome the limitations of traditional RSSI-based methods in unstable wireless environments.

Main Methods:

  • Harnessing AGV movement patterns (location, trajectory, orientation) to predict optimal AP connections.
  • Utilizing machine learning to learn location-, trajectory-, and orientation-specific RSSI from APs.
  • Validating the approach with real-world factory data from a unique dataset.

Main Results:

  • The proposed method extends potential communication duration per route by 1.34 times compared to conventional signal strength-based switching.
  • Demonstrated significant improvement over standard Wi-Fi drivers in communication stability.
  • Reduced the need for extensive manual investigation of wireless propagation paths.

Conclusions:

  • The novel machine learning-based AP selection technique effectively enhances AGV wireless communication in manufacturing.
  • The method offers a more robust and efficient solution for AP switching than current industry practices.
  • Automated evaluation and tuning of the wireless environment streamline AGV adaptation to existing AP infrastructure.