Autonomous Systems Engineering: AI at the Core of Smart Machines
Md Habibur Rahman1*, K. M. Sadat Samin2
Applied IT & Engineering 3(1) 1-8 https://doi.org/10.25163/engineering.3110238
Submitted: 03 November 2024 Revised: 18 January 2025 Published: 22 January 2025
Abstract
Autonomous systems engineering has emerged as a critical frontier in modern technology, placing artificial intelligence (AI) at the center of smart machine functionality. The development of autonomous systems, ranging from self-driving cars to uncrewed aerial vehicles and robotic assistants, relies heavily on AI for decision-making, perception, and adaptation to dynamic environments. This review synthesizes current research and practical advancements in autonomous systems engineering, emphasizing AI-driven methodologies such as machine learning, deep learning, and sensor fusion. The methods discussed include integrative engineering approaches that couple computational models with real-time system data. The results indicate a marked performance, reliability, and efficiency improvement across various applications. Advances in autonomous navigation, adaptive control, and fault detection have redefined what machines can accomplish without human intervention. The study highlights the transformative impact of AI-centric autonomous systems on industries such as transportation, manufacturing, and defense. It also reflects on the safety challenges, ethical concerns, and system validation, calling for interdisciplinary collaborations to guide future innovations. This article underscores that AI is not merely a component but the core enabler of autonomous capabilities, driving machines toward smarter, safer, and more responsive behaviours.
Keywords: Autonomous Systems, Artificial Intelligence, Machine Learning, Robotics, Sensor Fusion, system engineering.
References
Ababsa, F. Augmented Reality Application in Manufacturing Industry: Maintenance and Non-destructive Testing (NDT) Use Cases. In International Conference on Augmented Reality, Virtual Reality and Computer Graphics; Springer: Berlin/Heidelberg, Germany, 2020; pp. 333–344.
Addamani, R.; Ravindra, H.V.; Gayathri Devi, S.; Gonchikar, U. Assessment of Weld Bead Performance for Pulsed Gas Metal Arc Welding (P-GMAW) Using Acoustic Emission (AE) and Machine Vision (MV) Signals Through NDT Methods for SS 304 Material. In ASME International Mechanical Engineering Congress and Exposition; American Society of Mechanical Engineers: New York, NY, USA, 2020; Volume 84485, p. V02AT02A002.
Ahmed, H.; La, H.M.; Gucunski, N. Review of non-destructive civil infrastructure evaluation for bridges: State-of-the-art robotic platforms, sensors and algorithms. Sensors 2020, 20, 3954.
Azarpajouh, S.; Calderón Díaz, J.; Bueso Quan, S.; Taheri, H. Farm 4.0: A review of innovative smart dairy technologies and their applications for welfare assessment in dairy cattle. CAB Rev. 2021, 16, 1–9. [CrossRef]
Bigelow, T.A.; Schneider, B.; Taheri, H. Detection of pores in additive manufactured parts by near field response of laser-induced ultrasound. AIP Conf. Proc. 2019, 2102, 070002.
Chen, C.; Zhou, S.; Hu, G.; Jia, J.; Floor, G.W.; Sharath, D.; Menaka, M. Lamb wave detection and localisation of multiple discontinuities for plate-like structures based on DBSCAN and k-means. Mater. Eval. 2019, 77, 1439–1449.
Dawood, T.; Zhu, Z.; Zayed, T. Machine vision-based model for spalling detection and quantification in subway networks. Autom. Constr. 2017, 81, 149–160.
Du, G.; Li, J.; Wang, W.; Jiang, C.; Song, S. Detection and characterisation of stress-corrosion cracking on 304 stainless steel by electrochemical noise and acoustic emission techniques. Corros. Sci. 2011, 53, 2918–2926.
Gardner, P.; Fuentes, R.; Dervilis, N.; Mineo, C.; Pierce, S.; Cross, E.; Worden, K. Machine learning at structural
Gemander, F. Machine Learning: Basics and NDT Applications. 2019. Available online: online:https://wiki.tum.de/display/zfp/Machine+Learning%3A+Basics+and+NDT+Applications (accessed on 10 March 2022).
Harley, J.B.; Sparkman, D. Machine learning and NDE: Past, present, and future. AIP Conf. Proc. 2019, 2102, 090001.
health monitoring and non-destructive evaluation interface. Philos. Trans. R. Soc. A 2020, 378, 20190581.
Kim, J.G.; Jang, C.; Kang, S.S. Classification of ultrasonic signals of thermally aged cast austenitic stainless steel (CASS) using machine learning (ML) models. Nucl. Eng. Technol. 2022, 54, 1167–1174.
Koester, L.; Taheri, H.; Bond, L.J.; Barnard, D.; Grey, J. Additive manufacturing metrology: State of the art and needs assessment. AIP Conf. Proc. 2016, 1706, 130001.
Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 2012, 25, 1097–1105.
Kumar, N.P.; Patankar, V.; Kulkarni, M. Ultrasonic Gauging and Imaging of Metallic Tubes and Pipes: A Review; International Atomic Energy Agency (IAEA): Vienna, Austria, 2020.Sensors 2022, 22, 4055 16 of 17
Lee, H.; Lim, H.J.; Skinner, T.; Chattopadhyay, A.; Hall, A. Automated fatigue damage detection and classification technique for composite structures using Lamb waves and deep autoencoder. Mech. Syst. Signal Process. 2022, 163, 108148. Sensors 2022, 22, 4055 17 of 17
Lee, L.H.; Rajkumar, R.; Lo, L.H.; Wan, C.H.; Isa, D. Oil and gas pipeline failure prediction system using long-range ultrasonic transducers and Euclidean-Support Vector Machines classification approach. Expert Syst. Appl. 2013, 40, 1925–1934.
Li, T.J.; Chen, C.C.; Liu, J.J.; Shao, G.F.; Chan, C.C.K. A novel THz differential spectral clustering recognition method based on t-SNE. Discret. Dyn. Nat. Soc. 2020, 2020, 6787608.
Liu, Z.; Meyendorf, N.; Mrad, N. The role of data fusion in predictive maintenance using digital twin. AIP Conf. Proc. 2018, 1949, 020023.
Meyendorf, N.G.; Bond, L.J.; Curtis-Beard, J.; Heilmann, S.; Pal, S.; Schallert, R.; Scholz, H.; Wunderlich, C. Nde 4.0—Nde for the 21st Century—The Internet of Things and cyber physical systems will revolutionise Nde. In Proceedings of the 15th Asia Pacific Conference for Non-Destructive Testing (APCNDT2017), Singapore, 13–17 November 2017.
Moomen, A.; Ali, A.; Ramahi, O.M. Reducing sweeping frequencies in microwave NDT employing machine learning feature selection. Sensors 2016, 16, 559.
Noble, W.S. What is a support vector machine? Nat. Biotechnol. 2006, 24, 1565–1567.
Osman, A.; Duan, Y.; Kaftandjian, V. Applied Artificial Intelligence in NDE. In Handbook of Nondestructive Evaluation 4.0; Springer: Berlin/Heidelberg, Germany, 2021; pp. 1–35.
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830.
Pyle, R.J.; Bevan, R.L.; Hughes, R.R.; Rachev, R.K.; Ali, A.A.S.; Wilcox, P.D. Deep learning for ultrasonic crack characterisation in NDE. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 2020, 68, 1854–1865.
Radkowski, R.; Garrett, T.; Holland, S.D. 3d machine vision technology for automatic data integration of ultrasonic data. In Proceedings of the QNDE 2019: 46th Annual Review of Progress in Quantitative Nondestructive Evaluation, Portland, OR, USA, 14–18 July 2019.
Risheh, A.; Tavakolian, P.; Melinkov, A.; Mandelis, A. Infrared computer vision in non-destructive imaging: Sharp delineation of subsurface defect boundaries in enhanced truncated correlation photothermal coherence tomography images using K-means clustering. NDT E Int. 2022, 125, 102568.
Saeed, N.; Omar, M.A.; Abdulrahman, Y. A neural network approach for quantifying defect depth, for nondestructive testing thermograms. Infrared Phys. Technol. 2018, 94, 55–64.
Salazar, A.; Igual, J.; Vergara, L. Agglomerative clustering of defects in ultrasonic non-destructive testing using hierarchical mixtures of independent component analysers. In Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China, 6–11 July 2014; pp. 2042–2049.
Saleem, M.; Gutierrez, H. Using artificial neural network and non-destructive test for crack detection in concrete surrounding the embedded steel reinforcement. Struct. Concr. 2021, 22, 2849–2867.
Selim, H.; Delgado Prieto, M.; Trull, J.; Romeral, L.; Cojocaru, C. Laser ultrasound inspection based on wavelet transform and data clustering for defect estimation in metallic samples. Sensors 2019, 19, 573.
Shao, J.; Shi, H.; Du, D.; Wang, L.; Cao, H. Automatic weld defect detection in real-time X-ray images based on support vector machine. In Proceedings of the 4th International Congress on Image and Signal Processing, Shanghai, China, 15–17 October 2011; Volume 4, pp. 1842–1846.
Shrifan, N.H.; Akbar, M.F.; Isa, N.A.M. Prospect of using artificial intelligence for microwave nondestructive testing technique: A review. IEEE Access 2019, 7, 110628–110650.
Siegel, M. Automation for nondestructive inspection of aircraft. In Proceedings of the Conference on Intelligent Robots in Factory, Field, Space, and Service, Houston, TX, USA, 21–24 March 1994; p. 1223.
Sophian, A.; Tian, G.; Fan, M. Pulsed eddy current non-destructive testing and evaluation: A review. Chin. J. Mech. Eng. 2017, 30, 500–514.
Taheri, H.; Koester, L.; Bigelow, T.; Bond, L.J. Finite element simulation and experimental verification of ultrasonic non-destructive inspection of defects in additively manufactured materials. AIP Conf. Proc. 2018, 1949, 020011.
Taheri, H.; Koester, L.W.; Bigelow, T.A.; Bond, L.J. Thermoelastic finite element modelling of laser-generated ultrasound in additive manufacturing materials. In Proceedings of the ASNT Annual Conference, Nashville, TN, USA, 30 October–2 November 2017; Volume 2017, pp. 188–198.
Taheri, H.; Koester, L.W.; Bigelow, T.A.; Faierson, E.J.; Bond, L.J. In situ additive manufacturing process monitoring with an acoustic technique: Clustering performance evaluation using K-means algorithm. J. Manuf. Sci. Eng. 2019, 141, 041011.
Tang, Y.; Lin, Y.; Huang, X.; Yao, M.; Huang, Z.; Zou, X. Grand challenges of machine-vision technology in civil structural health monitoring. In Artificial Intelligence Evolution; Universal Wiser Publisher: Singapore, 2020; pp. 8–16.
Vejdannik, M.; Sadr, A.; de Albuquerque, V.H.C.; Tavares, J.M.R. Signal processing for NDE. In Handbook of Advanced Non-Destructive Evaluation; Springer: Cham, Switzerland, 2018.
Volker, C.; Kruschwitz, S.; Boller, C.; Wiggenhauser, H. Feasibility study on adapting a machine learning based multi-sensor data fusion approach for honeycomb detection in concrete. In NDE/NDT for Highways & Bridges: SMT 2016; The American Society of Nondestructive Testing: Columbus, OH, USA, 2016; pp. 144–148.
Vrana, J.; Meyendorf, N.; Ida, N.; Singh, R. Introduction to NDE 4.0. In Handbook of Nondestructive Evaluation 4.0; Springer: Berlin/Heidelberg, Germany, 2021; pp. 1–28.
Wunderlich, C.; Tschöpe, C.; Duckhorn, F. Advanced methods in NDE using machine learning approaches. AIP Conf. Proc. 2018, 1949, 020022.
Yadavar Nikravesh, S.M.; Rezaie, H.; Kilpatrik, M.; Taheri, H. Intelligent fault diagnosis of bearings based on energy levels in frequency bands using wavelet and support vector machines (SVM). J. Manuf. Mater. Process. 2019, 3, 11.
Ye, X.W.; Dong, C.Z.; Liu, T. A review of machine vision-based structural health monitoring: Methodologies and applications. J. Sensors 2016, 2016, 7103039.
Zhou, X.; Wang, H.; Hsieh, S.J.T. Thermography and k-means clustering methods for anti-reflective coating film inspection: Scratch and bubble defects. In Thermosense: Thermal Infrared Applications XXXVIII; SPIE: Bellingham, WA, USA, 2016; Volume 9861, pp. 195–204.
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