Next Article in Journal
AHA: Design and Evaluation of Compute-Intensive Hardware Accelerators for AMD-Xilinx Zynq SoCs Using HLS IP Flow
Previous Article in Journal
Interpretable Deep Learning for Diabetic Retinopathy: A Comparative Study of CNN, ViT, and Hybrid Architectures
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Computers
Volume 14
Issue 5
10.3390/computers14050188
computers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments

by
Athanasios Sidiropoulos
1,
Dimitrios Konstantinidis
2,
Xenofon Karamanos
1,
Theofilos Mastos
3,
Konstantinos Apostolou
4,
Theocharis Chatzis
2,
Maria Papaspyropoulou
4,
Kalliroi Marini
4,
Georgios Karamitsos
1,
Christina Theodoridou
2,
Andreas Kargakos
2,
Matina Vogiatzi
3,
Angelos Papadopoulos
3,
Dimitrios Giakoumis
2,
Dimitrios Bechtsis
1,
Kosmas Dimitropoulos
2 and
Dimitrios Vlachos
1,*
1
Laboratory of Statistics and Quantitative Analysis Methods, Department of Industrial Management, School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
Centre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece
3
KLEEMANN HELLAS SA, Industrial Area of Kilkis, 61100 Kilkis, Greece
4
Atlantis Engineering SA, 12th km Thessaloniki-Moudania, 57001 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Computers 2025, 14(5), 188; https://doi.org/10.3390/computers14050188
Submission received: 15 April 2025 / Revised: 7 May 2025 / Accepted: 9 May 2025 / Published: 12 May 2025
Versions Notes

Abstract

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.
Keywords: autonomous mobile robots; machine learning; computer vision; production control; worker safety autonomous mobile robots; machine learning; computer vision; production control; worker safety

Share and Cite

MDPI and ACS Style

Sidiropoulos, A.; Konstantinidis, D.; Karamanos, X.; Mastos, T.; Apostolou, K.; Chatzis, T.; Papaspyropoulou, M.; Marini, K.; Karamitsos, G.; Theodoridou, C.; et al. A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments. Computers 2025, 14, 188. https://doi.org/10.3390/computers14050188

AMA Style

Sidiropoulos A, Konstantinidis D, Karamanos X, Mastos T, Apostolou K, Chatzis T, Papaspyropoulou M, Marini K, Karamitsos G, Theodoridou C, et al. A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments. Computers. 2025; 14(5):188. https://doi.org/10.3390/computers14050188

Chicago/Turabian Style

Sidiropoulos, Athanasios, Dimitrios Konstantinidis, Xenofon Karamanos, Theofilos Mastos, Konstantinos Apostolou, Theocharis Chatzis, Maria Papaspyropoulou, Kalliroi Marini, Georgios Karamitsos, Christina Theodoridou, and et al. 2025. "A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments" Computers 14, no. 5: 188. https://doi.org/10.3390/computers14050188

APA Style

Sidiropoulos, A., Konstantinidis, D., Karamanos, X., Mastos, T., Apostolou, K., Chatzis, T., Papaspyropoulou, M., Marini, K., Karamitsos, G., Theodoridou, C., Kargakos, A., Vogiatzi, M., Papadopoulos, A., Giakoumis, D., Bechtsis, D., Dimitropoulos, K., & Vlachos, D. (2025). A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments. Computers, 14(5), 188. https://doi.org/10.3390/computers14050188

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop