Automation of unstructured production environment by applying reinforcement learning
Implementation of Machine Learning (ML) to improve product and production development processes poses a significant opportunity for manufacturing industries. ML has the capability to calibrate models with considerable adaptability and high accuracy. This capability is specifically promising for appl...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Manufacturing Technology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmtec.2023.1154263/full |
| _version_ | 1797869981551034368 |
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| author | Sanjay Nambiar Anton Wiberg Mehdi Tarkian |
| author_facet | Sanjay Nambiar Anton Wiberg Mehdi Tarkian |
| author_sort | Sanjay Nambiar |
| collection | DOAJ |
| description | Implementation of Machine Learning (ML) to improve product and production development processes poses a significant opportunity for manufacturing industries. ML has the capability to calibrate models with considerable adaptability and high accuracy. This capability is specifically promising for applications where classical production automation is too expensive, e.g., for mass customization cases where the production environment is uncertain and unstructured. To cope with the diversity in production systems and working environments, Reinforcement Learning (RL) in combination with lightweight game engines can be used from initial stages of a product and production development process. However, there are multiple challenges such as collecting observations in a virtual environment which can interact similar to a physical environment. This project focuses on setting up RL methodologies to perform path-finding and collision detection in varying environments. One case study is human assembly evaluation method in the automobile industry which is currently manual intensive to investigate digitally. For this case, a mannequin is trained to perform pick and place operations in varying environments and thus automating assembly validation process in early design phases. The next application is path-finding of mobile robots including an articulated arm to perform pick and place operations. This application is expensive to setup with classical methods and thus RL enables an automated approach for this task as well. |
| first_indexed | 2024-04-10T00:20:08Z |
| format | Article |
| id | doaj.art-84218694813446929d6ceb10b9cc6ea2 |
| institution | Directory Open Access Journal |
| issn | 2813-0359 |
| language | English |
| last_indexed | 2024-04-10T00:20:08Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Manufacturing Technology |
| spelling | doaj.art-84218694813446929d6ceb10b9cc6ea22023-03-16T04:42:44ZengFrontiers Media S.A.Frontiers in Manufacturing Technology2813-03592023-03-01310.3389/fmtec.2023.11542631154263Automation of unstructured production environment by applying reinforcement learningSanjay NambiarAnton WibergMehdi TarkianImplementation of Machine Learning (ML) to improve product and production development processes poses a significant opportunity for manufacturing industries. ML has the capability to calibrate models with considerable adaptability and high accuracy. This capability is specifically promising for applications where classical production automation is too expensive, e.g., for mass customization cases where the production environment is uncertain and unstructured. To cope with the diversity in production systems and working environments, Reinforcement Learning (RL) in combination with lightweight game engines can be used from initial stages of a product and production development process. However, there are multiple challenges such as collecting observations in a virtual environment which can interact similar to a physical environment. This project focuses on setting up RL methodologies to perform path-finding and collision detection in varying environments. One case study is human assembly evaluation method in the automobile industry which is currently manual intensive to investigate digitally. For this case, a mannequin is trained to perform pick and place operations in varying environments and thus automating assembly validation process in early design phases. The next application is path-finding of mobile robots including an articulated arm to perform pick and place operations. This application is expensive to setup with classical methods and thus RL enables an automated approach for this task as well.https://www.frontiersin.org/articles/10.3389/fmtec.2023.1154263/fullreinforcement learningunity game enginemobile robotmannequinproduction environmentpath-finding |
| spellingShingle | Sanjay Nambiar Anton Wiberg Mehdi Tarkian Automation of unstructured production environment by applying reinforcement learning Frontiers in Manufacturing Technology reinforcement learning unity game engine mobile robot mannequin production environment path-finding |
| title | Automation of unstructured production environment by applying reinforcement learning |
| title_full | Automation of unstructured production environment by applying reinforcement learning |
| title_fullStr | Automation of unstructured production environment by applying reinforcement learning |
| title_full_unstemmed | Automation of unstructured production environment by applying reinforcement learning |
| title_short | Automation of unstructured production environment by applying reinforcement learning |
| title_sort | automation of unstructured production environment by applying reinforcement learning |
| topic | reinforcement learning unity game engine mobile robot mannequin production environment path-finding |
| url | https://www.frontiersin.org/articles/10.3389/fmtec.2023.1154263/full |
| work_keys_str_mv | AT sanjaynambiar automationofunstructuredproductionenvironmentbyapplyingreinforcementlearning AT antonwiberg automationofunstructuredproductionenvironmentbyapplyingreinforcementlearning AT mehditarkian automationofunstructuredproductionenvironmentbyapplyingreinforcementlearning |