<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The COVID-19 pandemic has had a massive impact on the global aviation industry. As a result, the airline industry has been forced to embrace new technologies and procedures in order to provide a more secure and bio-safe travel. Currently, the role of smart technology in airport systems has expanded significantly as a result of the contemporary Industry 4.0 context. The article presents a novel construction of an intelligent mobile robot system to guide passengers to take the plane at the departure terminals at busy airports. The robot provides instructions to the customer through the interaction between the robot and the customer utilizing voice communications. The usage of the Google Cloud Speech-to-Text API combined with technical machine learning to analyze and understand the customer's requirements are deployed. In addition, we use a face detection technique based on Multi-task Cascaded Convolutional Networks (MTCNN) to predict the distance between the robot and passengers to perform the function. The robot can guide passengers to desired areas in the terminal. The results and evaluation of the implementation process are also mentioned in the article and show promise.</p>
</abstract>

The COVID-19 pandemic has had a massive impact on the global aviation industry. As a result, the airline industry has been forced to embrace new technologies and procedures in order to provide a more secure and bio-safe travel. Currently, the role of smart technology in airport systems has expanded significantly as a result of the contemporary Industry 4.0 context. The article presents a novel construction of an intelligent mobile robot system to guide passengers to take the plane at the departure terminals at busy airports. The robot provides instructions to the customer through the interaction between the robot and the customer utilizing voice communications. The usage of the Google Cloud Speech-to-Text API combined with technical machine learning to analyze and understand the customer's requirements are deployed. In addition, we use a face detection technique based on Multi-task Cascaded Convolutional Networks (MTCNN) to predict the distance between the robot and passengers to perform the function. The robot can guide passengers to desired areas in the terminal. The results and evaluation of the implementation process are also mentioned in the article and show promise.

A novel design of a smart interactive guiding robot for busy airports

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A novel design of a smart interactive guiding robot for busy airports"}

The COVID-19 pandemic has had a massive impact on the global aviation industry. As a result, the airline industry has been forced to embrace new technologies...

A novel design of a smart interactive guiding robot for busy airports

Article Category: Article

Publié en ligne: 09 nov. 2022

Pages: -

Reçu: 06 août 2022

DOI: https://doi.org/10.2478/ijssis-2022-0017

Mots clés
Busy airport, Service robot, Multi-task Cascaded Convolutional Networks (MTCNN), Computer vision, Prediction, Distance adjustment, Hall sensor, Google Cloud Speech-to-Text, Voice recognition

© 2022 Hoang T. Tran et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A novel design of a smart interactive guiding robot for busy airports

Article Category: Article

Publié en ligne: 09 nov. 2022

Pages: -

Reçu: 06 août 2022

DOI: https://doi.org/10.2478/ijssis-2022-0017

Mots clésBusy airport, Service robot, Multi-task Cascaded Convolutional Networks (MTCNN), Computer vision, Prediction, Distance adjustment, Hall sensor, Google Cloud Speech-to-Text, Voice recognition

© 2022 Hoang T. Tran et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Busy airport, Service robot, Multi-task Cascaded Convolutional Networks (MTCNN), Computer vision, Prediction, Distance adjustment, Hall sensor, Google Cloud Speech-to-Text, Voice recognition