Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer
The use of augmented reality (AR) technology is growing in the maintenance industry because it can improve efficiency and reduce costs by providing real-time guidance and instruction to workers during repairs and maintenance tasks. AR can also assist with equipment training and visualization, allowi...
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| Format: | Article |
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MDPI AG
2023-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/13/6024 |
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| author | Hibah Alatawi Nouf Albalawi Ghadah Shahata Khulud Aljohani A’aeshah Alhakamy Mihran Tuceryan |
| author_facet | Hibah Alatawi Nouf Albalawi Ghadah Shahata Khulud Aljohani A’aeshah Alhakamy Mihran Tuceryan |
| author_sort | Hibah Alatawi |
| collection | DOAJ |
| description | The use of augmented reality (AR) technology is growing in the maintenance industry because it can improve efficiency and reduce costs by providing real-time guidance and instruction to workers during repairs and maintenance tasks. AR can also assist with equipment training and visualization, allowing users to explore the equipment’s internal structure and size. The adoption of AR in maintenance is expected to increase as hardware options expand and development costs decrease. To implement AR for job aids in mobile applications, 3D spatial information and equipment details must be addressed, and calibrated using image-based or object-based tracking, which is essential for integrating 3D models with physical components. The present paper suggests a system using AR-assisted deep reinforcement learning (RL)-based model for NanoDrop Spectrophotometer training and maintenance purposes that can be used for rapid repair procedures in the Industry 4.0 (I4.0) setting. The system uses a camera to detect the target asset via feature matching, tracking techniques, and 3D modeling. Once the detection is completed, AR technologies generate clear and easily understandable instructions for the maintenance operator’s device. According to the research findings, the model’s target technique resulted in a mean reward of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.000</mn></mrow></semantics></math></inline-formula> and a standard deviation of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.000</mn></mrow></semantics></math></inline-formula>. This means that all the rewards that were obtained in the given task or environment were exactly the same. The fact that the reward standard deviation is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.000</mn></mrow></semantics></math></inline-formula> shows that there is no variability in the outcomes. |
| first_indexed | 2024-03-11T01:29:26Z |
| format | Article |
| id | doaj.art-77d8a2b2485e46bf8a9be51fc39687a0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:29:26Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-77d8a2b2485e46bf8a9be51fc39687a02023-11-18T17:30:18ZengMDPI AGSensors1424-82202023-06-012313602410.3390/s23136024Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop SpectrophotometerHibah Alatawi0Nouf Albalawi1Ghadah Shahata2Khulud Aljohani3A’aeshah Alhakamy4Mihran Tuceryan5Department of Computer Science, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi ArabiaDepartment of Computer Science, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi ArabiaDepartment of Computer Science, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi ArabiaDepartment of Computer Science, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi ArabiaDepartment of Computer Science, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi ArabiaDepartment of Computer Science, School of Science, Indiana University-Purdue University, Indianapolis, IN 46202, USAThe use of augmented reality (AR) technology is growing in the maintenance industry because it can improve efficiency and reduce costs by providing real-time guidance and instruction to workers during repairs and maintenance tasks. AR can also assist with equipment training and visualization, allowing users to explore the equipment’s internal structure and size. The adoption of AR in maintenance is expected to increase as hardware options expand and development costs decrease. To implement AR for job aids in mobile applications, 3D spatial information and equipment details must be addressed, and calibrated using image-based or object-based tracking, which is essential for integrating 3D models with physical components. The present paper suggests a system using AR-assisted deep reinforcement learning (RL)-based model for NanoDrop Spectrophotometer training and maintenance purposes that can be used for rapid repair procedures in the Industry 4.0 (I4.0) setting. The system uses a camera to detect the target asset via feature matching, tracking techniques, and 3D modeling. Once the detection is completed, AR technologies generate clear and easily understandable instructions for the maintenance operator’s device. According to the research findings, the model’s target technique resulted in a mean reward of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.000</mn></mrow></semantics></math></inline-formula> and a standard deviation of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.000</mn></mrow></semantics></math></inline-formula>. This means that all the rewards that were obtained in the given task or environment were exactly the same. The fact that the reward standard deviation is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.000</mn></mrow></semantics></math></inline-formula> shows that there is no variability in the outcomes.https://www.mdpi.com/1424-8220/23/13/6024augmented realityextended realityreinforcement learningtrainingmaintenancelocalization |
| spellingShingle | Hibah Alatawi Nouf Albalawi Ghadah Shahata Khulud Aljohani A’aeshah Alhakamy Mihran Tuceryan Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer Sensors augmented reality extended reality reinforcement learning training maintenance localization |
| title | Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer |
| title_full | Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer |
| title_fullStr | Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer |
| title_full_unstemmed | Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer |
| title_short | Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer |
| title_sort | augmented reality assisted deep reinforcement learning based model towards industrial training and maintenance for nanodrop spectrophotometer |
| topic | augmented reality extended reality reinforcement learning training maintenance localization |
| url | https://www.mdpi.com/1424-8220/23/13/6024 |
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