Machine Learning-based RSSI Prediction in Factory Environments

Webber, Julian; Suga, Norisato; Ano, Shigehiro; Hou, Yafei; Mehbodniya, Abolfazl; Higashimori, Toshihide; Yano, Kazuto

doi:10.1109/apcc47188.2019.9026476

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Figure 3 presents a performance comparison between KM-DF used in this study and the window function filtering method [13]. The window function method utilized simulation by selecting the measured values within the 0.4s-0.6s interval as the training set for parameter adjustments.…”

Section: Simulation Of Km-dfmentioning

confidence: 99%

The underwater dynamic environment RSSI ranging filtering algorithm

fu,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Section: Simulation Of Km-dfmentioning

confidence: 99%

The underwater dynamic environment RSSI ranging filtering algorithm

fu,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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“…Lastly, in [25], the authors consider a mobile robot moving indoors and use an infrastructure of multiple static receivers to predict the RSSI profile of the mobile link. While their scenario shares a lot of similarities with the one we focus on this paper, the reliance on multiple static receiver nodes makes it inapplicable to outdoor settings where such infrastructure does not exist, such as agricultural scenarios.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Wireless Connectivity Prediction for Connected Agricultural Vehicles

Grétarsson,

Orfanidis,

Marchegiani

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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“…For reliability of interaction between AGVs that are relying on communication networks, authors in [242] focuses on the need to minimize packet errors to ensure quality of control (QoC) and thus improve on system reliability. Researchers in [243] studied the prediction of RSSI at a receiver that tracks an AGV as it moves along a factory floor. Machine learning was used with a sliding window pattern of RSSI signal leading to further improvement of prediction performance by multiple AGVs.…”

Section: Also Discussed the Design Of A Cheap Wi-fi Based Agv Called Cheap Cooperative Agv (Ccagv) Ccagv Uses The Esp8266mentioning

confidence: 99%

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

et al. 2020

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In industrial environments, over several decades, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have served to improve efficiencies of intralogistics and material handling tasks. However, for system integrators, the choice and effective deployment of improved, suitable and reliable communication and control technologies for these unmanned vehicles remains a very challenging task. Specifics of communication for AGVs and AMRs imposes stringent performance requirements on latency and reliability of communication links which many existing wireless technologies struggle to satisfy. In this paper, a review of latest AGVs and AMRs research results in the past decade is presented. The review encompasses results from different past and present research domains of AGVs. In addition, performance requirements of communication networks in terms of their latencies and reliabilities when they are deployed for AGVs and AMRs coordination, control and fleet management in smart manufacturing environments are discussed. Integration challenges and limitations of present state-of-the-art AGV and AMR technologies when those technologies are used for facilitating AGV-based smart manufacturing and factory of the future applications are also thoroughly discussed. The paper also present a thorough discussion of areas in need of further research regarding the application of 5G networks for AGVs and AMRs fleet management in smart manufacturing environments. In addition, novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.INDEX TERMS Intelligent factory, factory of the future, 5G, smart manufacturing, industry 4.0, autonomous industrial equipment, AGV, AMR, tactile Internet, virtual reality, lean manufacturing.

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Machine Learning-based RSSI Prediction in Factory Environments

Cited by 8 publications

References 13 publications

The underwater dynamic environment RSSI ranging filtering algorithm

The underwater dynamic environment RSSI ranging filtering algorithm

Short-Term Wireless Connectivity Prediction for Connected Agricultural Vehicles

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

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