Virtual, Augmented, and Mixed Reality Robotics-Assisted Deep Reinforcement Learning Towards Smart Manufacturing
Welding robots are essential in modern manufacturing, providing high precision and efficiency in welding processes. To optimize their performance and minimize errors, accurate simulation of their behavior is crucial. This paper presents a novel approach to enhance the simulation of welding robots us...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3349 |
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| author | Than Le Le Quang Vinh Van Huy Pham |
| author_facet | Than Le Le Quang Vinh Van Huy Pham |
| author_sort | Than Le |
| collection | DOAJ |
| description | Welding robots are essential in modern manufacturing, providing high precision and efficiency in welding processes. To optimize their performance and minimize errors, accurate simulation of their behavior is crucial. This paper presents a novel approach to enhance the simulation of welding robots using the Virtual, Augmented, and Mixed Reality (VAM) simulation platform. The VAM platform offers a dynamic and versatile environment that enables a detailed and realistic representation of welding robot actions, interactions, and responses. By integrating VAM with existing simulation techniques, we aim to improve the fidelity and realism of the simulations. Furthermore, to accelerate the learning and optimization of the welding robot’s behavior, we incorporate deep reinforcement learning (DRL) techniques. Specifically, DRL is utilized for task offloading and trajectory planning, allowing the robot to make intelligent decisions in real-time. This integration not only enhances the simulation’s accuracy but also improves the robot’s operational efficiency in smart manufacturing environments. Our approach demonstrates the potential of combining advanced simulation platforms with machine learning to advance the capabilities of industrial robots. In addition, experimental results show that ANFIS achieves higher accuracy and faster convergence compared to traditional control strategies such as PID and FLC. |
| format | Article |
| id | doaj-art-2738eae2c2aa4dd9b3a289236c81f892 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-2738eae2c2aa4dd9b3a289236c81f8922025-08-20T03:11:24ZengMDPI AGSensors1424-82202025-05-012511334910.3390/s25113349Virtual, Augmented, and Mixed Reality Robotics-Assisted Deep Reinforcement Learning Towards Smart ManufacturingThan Le0Le Quang Vinh1Van Huy Pham2Institute of Engineering and Technology, Thu Dau Mot University, Thu Dau Mot 75100, VietnamWisdom Research, Ho Chi Minh City 700000, VietnamFaculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, VietnamWelding robots are essential in modern manufacturing, providing high precision and efficiency in welding processes. To optimize their performance and minimize errors, accurate simulation of their behavior is crucial. This paper presents a novel approach to enhance the simulation of welding robots using the Virtual, Augmented, and Mixed Reality (VAM) simulation platform. The VAM platform offers a dynamic and versatile environment that enables a detailed and realistic representation of welding robot actions, interactions, and responses. By integrating VAM with existing simulation techniques, we aim to improve the fidelity and realism of the simulations. Furthermore, to accelerate the learning and optimization of the welding robot’s behavior, we incorporate deep reinforcement learning (DRL) techniques. Specifically, DRL is utilized for task offloading and trajectory planning, allowing the robot to make intelligent decisions in real-time. This integration not only enhances the simulation’s accuracy but also improves the robot’s operational efficiency in smart manufacturing environments. Our approach demonstrates the potential of combining advanced simulation platforms with machine learning to advance the capabilities of industrial robots. In addition, experimental results show that ANFIS achieves higher accuracy and faster convergence compared to traditional control strategies such as PID and FLC.https://www.mdpi.com/1424-8220/25/11/3349arc welding robotVAMdigital twinsunityARsimulation |
| spellingShingle | Than Le Le Quang Vinh Van Huy Pham Virtual, Augmented, and Mixed Reality Robotics-Assisted Deep Reinforcement Learning Towards Smart Manufacturing Sensors arc welding robot VAM digital twins unity AR simulation |
| title | Virtual, Augmented, and Mixed Reality Robotics-Assisted Deep Reinforcement Learning Towards Smart Manufacturing |
| title_full | Virtual, Augmented, and Mixed Reality Robotics-Assisted Deep Reinforcement Learning Towards Smart Manufacturing |
| title_fullStr | Virtual, Augmented, and Mixed Reality Robotics-Assisted Deep Reinforcement Learning Towards Smart Manufacturing |
| title_full_unstemmed | Virtual, Augmented, and Mixed Reality Robotics-Assisted Deep Reinforcement Learning Towards Smart Manufacturing |
| title_short | Virtual, Augmented, and Mixed Reality Robotics-Assisted Deep Reinforcement Learning Towards Smart Manufacturing |
| title_sort | virtual augmented and mixed reality robotics assisted deep reinforcement learning towards smart manufacturing |
| topic | arc welding robot VAM digital twins unity AR simulation |
| url | https://www.mdpi.com/1424-8220/25/11/3349 |
| work_keys_str_mv | AT thanle virtualaugmentedandmixedrealityroboticsassisteddeepreinforcementlearningtowardssmartmanufacturing AT lequangvinh virtualaugmentedandmixedrealityroboticsassisteddeepreinforcementlearningtowardssmartmanufacturing AT vanhuypham virtualaugmentedandmixedrealityroboticsassisteddeepreinforcementlearningtowardssmartmanufacturing |