Application of heuristic control technique to path planning of an autonomous guided vehicle

This paper proposes the use of the AHP-Gaussian method to support the selection of a smart sensor installation for an electric motor used in an escalator in a subway station. The AHP-Gaussian methodology utilizes the Analytic Hierarchy Process (AHP) framework and is highlighted for its ability to save the decision maker’s cognitive effort in assigning weights to criteria. Seven criteria were defined for the sensor selection: temperature range, vibration range, weight, communication distance, maximum electric power, data traffic speed, and acquisition cost. Four smart sensors were considered as alternatives. The results of the analysis showed that the most appropriate sensor was the ABB Ability smart sensor, which scored the highest in the AHP-Gaussian analysis. In addition, this sensor could detect any abnormalities in the equipment’s operation, enabling timely maintenance and preventing potential failures. The proposed AHP-Gaussian method proved to be an effective approach for selecting a smart sensor for an electric motor used in an escalator in a subway station. The selected sensor was reliable, accurate, and cost-effective, contributing to the safe and efficient operation of the equipment.

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“…efficiency [11][12][13][14][15][16][17][18][19]. The research of [20] confirms that sensors can perform such roles.…”

Section: Introductionmentioning

confidence: 88%

Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method

Pereira

Junior

Bandeira

et al. 2023

Sensors

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Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance?

Chiarini

2021

Bus Strat Env

117

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This research contributes to the debate about the relevance of Industry 4.0 technologies in improving environmental performance in the manufacturing industry. We employed a qualitative–quantitative approach in which 19 Italian operations managers were interviewed and 260 managers responded to an online questionnaire. The effects of various technologies were ranked using ordinal regression. Comments and suggestions gave context to the quantitative results. Sensors, radio‐frequency identification, artificial intelligence and analytics were found to be most relevant in improving environmental performance, whereas simulation software contributed moderately. Additive manufacturing, cobots, robots, automated mobile robots and automated guided vehicles had a negative effect, augmented reality had no effect and other technologies indirectly affected environmental performance. We also found a lack of knowledge and application as well as scepticism about technologies such as artificial intelligence and augmented reality. Finally, there was concern about the disposal of electrical and electronic waste produced by these technologies.

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Autonomous guided vehicle control using a self-organizing fuzzy controller

Lee¹,

Na²

2002

International Journal of Computer Integrated Manufacturing

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Application of heuristic control technique to path planning of an autonomous guided vehicle

Cited by 3 publications

References 1 publication

Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method

Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method

Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance?

Autonomous guided vehicle control using a self-organizing fuzzy controller

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