Exploiting visual cues for safe and flexible cyber-physical production systems
Human workers are envisioned to work alongside robots and other intelligent factory modules, and fulfill supervision tasks in future smart factories. Technological developments, during the last few years, in the field of smart factory automation have introduced the concept of cyber-physical systems,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2019-12-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/1687814019897228 |
| _version_ | 1818120238042972160 |
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| author | Syed Osama Bin Islam Waqas Akbar Lughmani Waqar Shahid Qureshi Azfar Khalid Miguel Angel Mariscal Susana Garcia-Herrero |
| author_facet | Syed Osama Bin Islam Waqas Akbar Lughmani Waqar Shahid Qureshi Azfar Khalid Miguel Angel Mariscal Susana Garcia-Herrero |
| author_sort | Syed Osama Bin Islam |
| collection | DOAJ |
| description | Human workers are envisioned to work alongside robots and other intelligent factory modules, and fulfill supervision tasks in future smart factories. Technological developments, during the last few years, in the field of smart factory automation have introduced the concept of cyber-physical systems, which further expanded to cyber-physical production systems. In this context, the role of collaborative robots is significant and depends largely on the advanced capabilities of collision detection, impedance control, and learning new tasks based on artificial intelligence. The system components, collaborative robots, and humans need to communicate for collective decision-making. This requires processing of shared information keeping in consideration the available knowledge, reasoning, and flexible systems that are resilient to the real-time dynamic changes on the industry floor as well as within the communication and computer network infrastructure. This article presents an ontology-based approach to solve industrial scenarios for safety applications in cyber-physical production systems. A case study of an industrial scenario is presented to validate the approach in which visual cues are used to detect and react to dynamic changes in real time. Multiple scenarios are tested for simultaneous detection and prioritization to enhance the learning surface of the intelligent production system with the goal to automate safety-based decisions. |
| first_indexed | 2024-12-11T05:22:55Z |
| format | Article |
| id | doaj.art-09707235f73f4c8ca770bf502e084537 |
| institution | Directory Open Access Journal |
| issn | 1687-8140 |
| language | English |
| last_indexed | 2024-12-11T05:22:55Z |
| publishDate | 2019-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-09707235f73f4c8ca770bf502e0845372022-12-22T01:19:38ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402019-12-011110.1177/1687814019897228Exploiting visual cues for safe and flexible cyber-physical production systemsSyed Osama Bin Islam0Waqas Akbar Lughmani1Waqar Shahid Qureshi2Azfar Khalid3Miguel Angel Mariscal4Susana Garcia-Herrero5Department of Mechanical Engineering, Capital University of Science and Technology (CUST), Islamabad, PakistanDepartment of Mechanical Engineering, Capital University of Science and Technology (CUST), Islamabad, PakistanRobot Design and Development Lab, National Center of Robotics and Automation, NUST College of Electrical and Mechanical Engineering, Rawalpindi, PakistanDepartment of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, UKDepartment of Civil Engineering, Universidad de Burgos, Burgos, SpainDepartment of Civil Engineering, Universidad de Burgos, Burgos, SpainHuman workers are envisioned to work alongside robots and other intelligent factory modules, and fulfill supervision tasks in future smart factories. Technological developments, during the last few years, in the field of smart factory automation have introduced the concept of cyber-physical systems, which further expanded to cyber-physical production systems. In this context, the role of collaborative robots is significant and depends largely on the advanced capabilities of collision detection, impedance control, and learning new tasks based on artificial intelligence. The system components, collaborative robots, and humans need to communicate for collective decision-making. This requires processing of shared information keeping in consideration the available knowledge, reasoning, and flexible systems that are resilient to the real-time dynamic changes on the industry floor as well as within the communication and computer network infrastructure. This article presents an ontology-based approach to solve industrial scenarios for safety applications in cyber-physical production systems. A case study of an industrial scenario is presented to validate the approach in which visual cues are used to detect and react to dynamic changes in real time. Multiple scenarios are tested for simultaneous detection and prioritization to enhance the learning surface of the intelligent production system with the goal to automate safety-based decisions.https://doi.org/10.1177/1687814019897228 |
| spellingShingle | Syed Osama Bin Islam Waqas Akbar Lughmani Waqar Shahid Qureshi Azfar Khalid Miguel Angel Mariscal Susana Garcia-Herrero Exploiting visual cues for safe and flexible cyber-physical production systems Advances in Mechanical Engineering |
| title | Exploiting visual cues for safe and flexible cyber-physical production systems |
| title_full | Exploiting visual cues for safe and flexible cyber-physical production systems |
| title_fullStr | Exploiting visual cues for safe and flexible cyber-physical production systems |
| title_full_unstemmed | Exploiting visual cues for safe and flexible cyber-physical production systems |
| title_short | Exploiting visual cues for safe and flexible cyber-physical production systems |
| title_sort | exploiting visual cues for safe and flexible cyber physical production systems |
| url | https://doi.org/10.1177/1687814019897228 |
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