Estimating Wireless Link Quality using Multiple Remote Sensors for Wireless Control of AGV in a Factory

Ohori, Fumiko; Itaya, Satoko; Osuga, Toru; Kojima, Fumihide

doi:10.1109/wpmc50192.2020.9309467

Cited by 10 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In locations such as manufacturing sites where rapid wireless fluctuations often occur, wireless sensing technologies for collecting, analyzing, and utilizing wireless environment information are envisioned to enable mobile devices like AGVs to use wireless communication in a stable manner [8,9]. When AGVs travel while communicating with multiple wireless LAN APs in a wide indoor area, the sensing area also becomes wide.…”

Section: Related Workmentioning

confidence: 99%

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

Ohori,

Yamaguchi,

Itaya

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Ohori,

Yamaguchi,

Itaya

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Zhan and Yu presented in 2018 a survey on wireless communication technologies for AGVs in [6]. Ohori et al exalted the importance of handling correctly the wireless communication inherent in AGVs [7]. The authors 978-1-7281-2989-1/21/$31.00 ©2021 IEEE presented a survey about delay and throughput for longdistance communication of AGVs in [8].…”

Section: Related Work and Contextmentioning

confidence: 99%

Towards enhanced live visualization based on communication delay prediction for remote AGV operation

Bejarano

Atmojo

Blech

et al. 2021

2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )

View full text Add to dashboard Cite

This paper presents the development progress of a methodology to enhance the dynamic visualization of remote Automated Guided Vehicles (AGVs) using open source frameworks as Gazebo and ROS. The approach is based on a deterministic path pre-calculated by edge computing, accessible for a 3D web visualization cloud platform. The trajectory displayed is verified by a live position streaming from the AGV and a predicted communication delay value. By succeeding on the methodology proposed, it is expected to enhance the fidelity of the 3D live representation for every trajectory executed dynamically and autonomously by AGVs in the shop floor, leaving behind an initial scenario with low visualization fidelity due non-deterministic behavior of long-distance communication channels (including wireless networks essential for AGVs). This work aims to have an impact on improving the user experience of remote interfacing tools, supporting a web-based paradigm to visualize the behavior of cyber-physical systems in agile manufacturing.

show abstract

Dynamic Path Planning for QoS Improvement in Multiple Automated Guided Vehicles

Tanagi,

Adachi

2023

2023 VTS Asia Pacific Wireless Communications Symposium (APWCS)

View full text Add to dashboard Cite

Estimating Wireless Link Quality using Multiple Remote Sensors for Wireless Control of AGV in a Factory

Cited by 10 publications

References 5 publications

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

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

Towards enhanced live visualization based on communication delay prediction for remote AGV operation

Dynamic Path Planning for QoS Improvement in Multiple Automated Guided Vehicles

Contact Info

Product

Resources

About