A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments
Industry 4.0 has revolutionized the way companies manufacture, improve, and distribute their products through the use of new technologies, such as artificial intelligence, robotics, and machine learning. Autonomous Mobile Robots (AMRs), especially, have gained a lot of attention, supporting workers...
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MDPI AG
2025-05-01
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| author | Athanasios Sidiropoulos Dimitrios Konstantinidis Xenofon Karamanos Theofilos Mastos Konstantinos Apostolou Theocharis Chatzis Maria Papaspyropoulou Kalliroi Marini Georgios Karamitsos Christina Theodoridou Andreas Kargakos Matina Vogiatzi Angelos Papadopoulos Dimitrios Giakoumis Dimitrios Bechtsis Kosmas Dimitropoulos Dimitrios Vlachos |
| author_facet | Athanasios Sidiropoulos Dimitrios Konstantinidis Xenofon Karamanos Theofilos Mastos Konstantinos Apostolou Theocharis Chatzis Maria Papaspyropoulou Kalliroi Marini Georgios Karamitsos Christina Theodoridou Andreas Kargakos Matina Vogiatzi Angelos Papadopoulos Dimitrios Giakoumis Dimitrios Bechtsis Kosmas Dimitropoulos Dimitrios Vlachos |
| author_sort | Athanasios Sidiropoulos |
| collection | DOAJ |
| description | Industry 4.0 has revolutionized the way companies manufacture, improve, and distribute their products through the use of new technologies, such as artificial intelligence, robotics, and machine learning. Autonomous Mobile Robots (AMRs), especially, have gained a lot of attention, supporting workers with daily industrial tasks and boosting overall performance by delivering vital information about the status of the production line. To this end, this work presents the novel Q-CONPASS system that aims to introduce AMRs in production lines with the ultimate goal of gathering important information that can assist in production and safety control. More specifically, the Q-CONPASS system is based on an AMR equipped with a plethora of machine learning algorithms that enable the vehicle to safely navigate in a dynamic industrial environment, avoiding humans, moving machines, and stationary objects while performing important tasks. These tasks include the identification of the following: (i) missing objects during product packaging and (ii) extreme skeletal poses of workers that can lead to musculoskeletal disorders. Finally, the Q-CONPASS system was validated in a real-life environment (i.e., the lift manufacturing industry), showcasing the importance of collecting and processing data in real-time to boost productivity and improve the well-being of workers. |
| format | Article |
| id | doaj-art-25f9b9d2759c40dcae261b86bbf14921 |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-25f9b9d2759c40dcae261b86bbf149212025-08-20T03:47:53ZengMDPI AGComputers2073-431X2025-05-0114518810.3390/computers14050188A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial EnvironmentsAthanasios Sidiropoulos0Dimitrios Konstantinidis1Xenofon Karamanos2Theofilos Mastos3Konstantinos Apostolou4Theocharis Chatzis5Maria Papaspyropoulou6Kalliroi Marini7Georgios Karamitsos8Christina Theodoridou9Andreas Kargakos10Matina Vogiatzi11Angelos Papadopoulos12Dimitrios Giakoumis13Dimitrios Bechtsis14Kosmas Dimitropoulos15Dimitrios Vlachos16Laboratory of Statistics and Quantitative Analysis Methods, Department of Industrial Management, School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceCentre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, GreeceLaboratory of Statistics and Quantitative Analysis Methods, Department of Industrial Management, School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceKLEEMANN HELLAS SA, Industrial Area of Kilkis, 61100 Kilkis, GreeceAtlantis Engineering SA, 12th km Thessaloniki-Moudania, 57001 Thessaloniki, GreeceCentre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, GreeceAtlantis Engineering SA, 12th km Thessaloniki-Moudania, 57001 Thessaloniki, GreeceAtlantis Engineering SA, 12th km Thessaloniki-Moudania, 57001 Thessaloniki, GreeceLaboratory of Statistics and Quantitative Analysis Methods, Department of Industrial Management, School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceCentre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, GreeceCentre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, GreeceKLEEMANN HELLAS SA, Industrial Area of Kilkis, 61100 Kilkis, GreeceKLEEMANN HELLAS SA, Industrial Area of Kilkis, 61100 Kilkis, GreeceCentre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, GreeceLaboratory of Statistics and Quantitative Analysis Methods, Department of Industrial Management, School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceCentre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, GreeceLaboratory of Statistics and Quantitative Analysis Methods, Department of Industrial Management, School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceIndustry 4.0 has revolutionized the way companies manufacture, improve, and distribute their products through the use of new technologies, such as artificial intelligence, robotics, and machine learning. Autonomous Mobile Robots (AMRs), especially, have gained a lot of attention, supporting workers with daily industrial tasks and boosting overall performance by delivering vital information about the status of the production line. To this end, this work presents the novel Q-CONPASS system that aims to introduce AMRs in production lines with the ultimate goal of gathering important information that can assist in production and safety control. More specifically, the Q-CONPASS system is based on an AMR equipped with a plethora of machine learning algorithms that enable the vehicle to safely navigate in a dynamic industrial environment, avoiding humans, moving machines, and stationary objects while performing important tasks. These tasks include the identification of the following: (i) missing objects during product packaging and (ii) extreme skeletal poses of workers that can lead to musculoskeletal disorders. Finally, the Q-CONPASS system was validated in a real-life environment (i.e., the lift manufacturing industry), showcasing the importance of collecting and processing data in real-time to boost productivity and improve the well-being of workers.https://www.mdpi.com/2073-431X/14/5/188autonomous mobile robotsmachine learningcomputer visionproduction controlworker safety |
| spellingShingle | Athanasios Sidiropoulos Dimitrios Konstantinidis Xenofon Karamanos Theofilos Mastos Konstantinos Apostolou Theocharis Chatzis Maria Papaspyropoulou Kalliroi Marini Georgios Karamitsos Christina Theodoridou Andreas Kargakos Matina Vogiatzi Angelos Papadopoulos Dimitrios Giakoumis Dimitrios Bechtsis Kosmas Dimitropoulos Dimitrios Vlachos A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments Computers autonomous mobile robots machine learning computer vision production control worker safety |
| title | A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments |
| title_full | A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments |
| title_fullStr | A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments |
| title_full_unstemmed | A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments |
| title_short | A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments |
| title_sort | novel autonomous robotic vehicle based system for real time production and safety control in industrial environments |
| topic | autonomous mobile robots machine learning computer vision production control worker safety |
| url | https://www.mdpi.com/2073-431X/14/5/188 |
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