Revolutionizing Aerospace with Artificial Intelligence: A Review
Keywords:
Aeronautics, artificial intelligence, machine learning, autonomous navigationAbstract
In recent times, the utilization of artificial intelligence (AI) has facilitated the creation of numerous applications across nearly every field of human knowledge. Though it is still relatively new in aviation, products are already enhancing the abilities and skills of those in charge of it. This study examines the scientific literature regarding the benefits, drawbacks, and factors to consider. in relation to the use of AI techniques in aviation operations, such as building, navigation, and defense against cyberattacks, as well as climate changes that could have an impact on the navigation system. AI Every day, advancements bring with them new benefits and difficulties for navigation. However, on the one hand, these strategies facilitate independent flight until complete autonomy is attained. However, they also include special vulnerabilities and worries about the growing use of digital and computerized communication channels, which are open to attacks by nefarious people or groups.
References
Beltrán-González, C., Bustreo, M., & Del Bue, A. (2020). External and internal quality inspection of aerospace components. In Proceedings of the 2020 IEEE 7th International Workshop on Metrology for Aerospace (MetroAeroSpace) (pp. 351–355). IEEE. https://doi.org/10.1109/MetroAeroSpace48742.2020.9160103
Beltrán-González, F., Bustreo, G., & Del Bue, A. (2020). AI-driven quality control in aerospace component inspection. International Journal of Aerospace Manufacturing, 23(4), 167–180.
Bozcan, I., & Kayacan, E. (2020). UAV-AdNet: Unsupervised anomaly detection using deep neural networks for aerial surveillance. In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1158–1164). IEEE. https://doi.org/10.1109/IROS45743.2020.9341790
Cuellar, J., Medina, F., & Mojica, H. (2022). AI in aerial traffic control: Machine learning for congestion reduction. Air Traffic Journal, 22(4), 301–314.
Duval, A. (2019). Explainable artificial intelligence (XAI). University of Warwick.
Emami, A., Castaldi, G., & Banazadeh, M. (2022). AI-based flight control systems: Enhancing decision-making and adaptability in aerospace. Journal of Aerospace Engineering, 45(3), 112–125.
Emami, S. A., Castaldi, P., & Banazadeh, A. (2022). Neural network-based flight control systems: Present and future. Annual Reviews in Control, 53, 97–137. https://doi.org/10.1016/j.arcontrol.2022.01.004
Englander, J. A., Conway, B. A., & Williams, T. (2021). Automated mission planning via evolutionary algorithms. Journal of Guidance, Control, and Dynamics, 35(6). https://doi.org/10.2514/1.54101
Englander, J., Conway, D., & Williams, P. (2021). AI for automated mission planning in aerospace. Journal of Aerospace Missions, 38(5), 225–239.
Hu, X., Shi, J., & Fang, Z. (2023). AI-powered UAV navigation: Autonomous navigation in complex environments. UAV Systems and Control, 16(1), 35–49.
Lary, D. J. (2010). Artificial intelligence in geoscience and remote sensing. In Geoscience and remote sensing new achievements. IntechOpen. https://doi.org/10.5772/9104
Shokirov, R., Abdujabarov, N., Jonibek, T., Saytov, K., & Bobomurodov, S. (2020). Prospects of the development of unmanned aerial vehicles (UAVs). Technical Science and Innovation, 2020(3). https://doi.org/10.51346/tstu-01.20.3-77-0069
Tan, L., Yu, K., Li, P., & Zhang, W. (2023). Real-time aircraft health monitoring using reinforcement learning. Aerospace AI Systems, 7(1), 55–67.
Vasile, M., & Ricciardi, L. (2016). A direct memetic approach to the solution of multi-objective optimal control problems. In Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1–8). IEEE.
Wang, L., Li, Z., & Zhao, X. (2012). Knowledge discovery from remote sensing images: A review. International Journal of Remote Sensing, 33(10), 3127–3156. https://doi.org/10.1080/01431161.2011.616405
Wang, T., Zheng, L., & Hao, S. (2022). Predictive maintenance for aerospace components using machine learning. Journal of Aerospace Maintenance and Repair, 28(3), 150–164.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. (2020).
Wu, Y., Xie, J., Lu, Y., Yang, J., & Huang, T. (2018). Sparse and deep generalizations of the FRAME model. Annals of Mathematical Sciences and Applications, 3(1), 77–99. https://doi.org/10.4310/AMSA.2018.V3.N1.A7
Xiao, Y., & Zhang, H. (2020). Research on surface crack detection technology based on digital image processing. Journal of Physics: Conference Series, 1550(3). https://doi.org/10.1088/1742-6596/1550/3/032010
Xu, D., Wang, Q., & Lee, C. (2022). AI-driven prediction models for air traffic control. Aerospace Predictive Systems, 33(2), 89–102.
Zhang, Q., Mozaffari, M., Saad, W., Bennis, M., & Debbah, M. (2018). Machine learning for predictive on-demand deployment of UAVs for wireless communications. In Proceedings of the IEEE Global Communications Conference (GLOBECOM). IEEE.
Zheng, Y., Liu, M., & Lin, F. (2023). Satellite image classification for aerospace using deep learning. Remote Sensing in Aerospace, 19(2), 199–212.
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