DEVELOPMENT OF AN AUTONOMOUS ROBOTIC SYSTEM FOR INDUSTRIAL MAINTENANCE AND INSPECTION
Article Sidebar
Main Article Content
Аннотация:
The rapid digitalization of industrial environments has increased the demand for autonomous robotic systems capable of performing maintenance and inspection tasks with high reliability and safety. Industrial facilities such as power plants, oil and gas installations, manufacturing lines, and chemical plants often operate under hazardous, constrained, or inaccessible conditions, making human inspection costly and risky. Autonomous robotic systems provide an effective solution by combining advanced sensing technologies, artificial intelligence, and autonomous navigation. This paper analyzes the development of autonomous robotic systems for industrial maintenance and inspection, focusing on system architecture, sensing and perception methods, navigation and localization techniques, and decision-making algorithms. The study synthesizes existing empirical findings and industrial case studies to evaluate performance improvements in safety, cost reduction, and operational efficiency. The results demonstrate that autonomous robots significantly enhance inspection accuracy and reduce downtime, while remaining challenges include energy autonomy, perception robustness, and system integration.
Article Details
Как цитировать:
Библиографические ссылки:
Siciliano, B., Khatib, O. Springer Handbook of Robotics. Springer, 2016, pp. 223–245.
Corke, P. Robotics, Vision and Control. Springer, 2017, pp. 1–25.
Yang, G., et al. “Robotic Inspection of Pipelines: A Review.” IEEE Transactions on Industrial Electronics, 2019, Vol. 66(8), pp. 6789–6801.
Kagermann, H., Wahlster, W., Helbig, J. Recommendations for Implementing Industry 4.0. Acatech, 2013, pp. 13–29.
Mobley, R. An Introduction to Predictive Maintenance. Elsevier, 2002, pp. 45–67.
Siegwart, R., Nourbakhsh, I., Scaramuzza, D. Introduction to Autonomous Mobile Robots. MIT Press, 2011, pp. 89–132.
Thrun, S., Burgard, W., Fox, D. Probabilistic Robotics. MIT Press, 2005, pp. 249–276.
Cadena, C., et al. “Past, Present, and Future of SLAM.” IEEE Transactions on Robotics, 2016, Vol. 32(6), pp. 1309–1332.
LeCun, Y., Bengio, Y., Hinton, G. “Deep Learning.” Nature, 2015, Vol. 521, pp. 436–444.
Zhang, Y., et al. “Autonomous Robots for Industrial Inspection.” Robotics and Computer-Integrated Manufacturing, 2020, Vol. 61, pp. 101–118.
Tao, F., Zhang, M. “Digital Twin Shop-Floor.” IEEE Access, 2017, Vol. 5, pp. 20418–20427.
ISO 8373:2012. Robots and Robotic Devices – Vocabulary. ISO, 2012, pp. 1–20.