Real-time digital twin with reinforcement learning for industrial manipulator applications
Samaylal, Sandeep (2024)
2024
Tietotekniikan DI-ohjelma - Master's Programme in Information Technology
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2024-12-30
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-2024112710574
https://urn.fi/URN:NBN:fi:tuni-2024112710574
Tiivistelmä
The industrial sector is undergoing a transformative phase with the integration of advanced robotics and artificial intelligence (AI) technologies. This thesis, explores the synergistic application of digital twin technology and reinforcement learning in enhancing the efficiency and adaptability of robotic manipulators in industrial environments.
The core premise of this research focuses on addressing the limitations of manual programming methods in dynamic and complex industrial settings. Manual programming often lacks the adaptability and learning capabilities required for efficient operation in varied and unpredictable environments. The incorporation of reinforcement learning enables robotic manipulators to learn and adjust through interaction with their environment, thereby enhancing operational efficiency and minimizing the need for extensive programming efforts.
A digital twin is a digital virtual replica of a physical environment. This allows for the simulation, analysis, and optimization of robotic manipulator behaviours in a controlled, risk-free setting. The integration of digital twins with reinforcement learning enables the efficient training of robotic systems, allowing them to learn complex tasks and adapt to new scenarios without the physical wear and risks associated with real-world training and setting up the environment. The research methodology involves the development of a digital twin simulation environment, the application of reinforcement learning algorithms to robotic manipulators within this environment, and the discussing the importance of the transfer ability of learned task to real-world application. The study also examines the challenges associated with digital twin and reinforcement learning technologies for developing an application.
The expected outcomes include improved adaptability and efficiency of robotic manipulators in industrial applications, leading to a reduction in the time, costs, and resources required for programming robots for specific tasks. Additionally, enhanced safety and reliability in autonomous robotic operations are anticipated. This study aims to demonstrate the potential of reinforcement learning and digital twin technologies in transforming industrial robotics, contributing to more flexible, efficient, and intelligent development processes for robotic applications.
This thesis has significant implications for the future of industrial automation, offering a pathway to more adaptable, efficient, and intelligent robotic systems. By leveraging the latest advancements in AI and simulation technologies, it aims to contribute to the evolution of industrial robotics, paving the way for more advanced industrial solutions.
The core premise of this research focuses on addressing the limitations of manual programming methods in dynamic and complex industrial settings. Manual programming often lacks the adaptability and learning capabilities required for efficient operation in varied and unpredictable environments. The incorporation of reinforcement learning enables robotic manipulators to learn and adjust through interaction with their environment, thereby enhancing operational efficiency and minimizing the need for extensive programming efforts.
A digital twin is a digital virtual replica of a physical environment. This allows for the simulation, analysis, and optimization of robotic manipulator behaviours in a controlled, risk-free setting. The integration of digital twins with reinforcement learning enables the efficient training of robotic systems, allowing them to learn complex tasks and adapt to new scenarios without the physical wear and risks associated with real-world training and setting up the environment. The research methodology involves the development of a digital twin simulation environment, the application of reinforcement learning algorithms to robotic manipulators within this environment, and the discussing the importance of the transfer ability of learned task to real-world application. The study also examines the challenges associated with digital twin and reinforcement learning technologies for developing an application.
The expected outcomes include improved adaptability and efficiency of robotic manipulators in industrial applications, leading to a reduction in the time, costs, and resources required for programming robots for specific tasks. Additionally, enhanced safety and reliability in autonomous robotic operations are anticipated. This study aims to demonstrate the potential of reinforcement learning and digital twin technologies in transforming industrial robotics, contributing to more flexible, efficient, and intelligent development processes for robotic applications.
This thesis has significant implications for the future of industrial automation, offering a pathway to more adaptable, efficient, and intelligent robotic systems. By leveraging the latest advancements in AI and simulation technologies, it aims to contribute to the evolution of industrial robotics, paving the way for more advanced industrial solutions.