
The application of artificial intelligence in aerospace engineering
- 1 Joint institution, Shanghai Jiao Tong University, Shanghai, China
* Author to whom correspondence should be addressed.
Abstract
In recent years, there has been considerable interest in applying Artificial Intelligence (AI) in the field of aerospace engineering. However, the existing literature on this topic is not sufficiently comprehensive. This paper is purposed to solve this problem by providing a thorough analysis and overview of the current state of AI in aerospace engineering. The paper is divided into four sections. Firstly, the use of AI in autonomous navigation and flight control is explored, focusing on advanced algorithms and sensor technologies that enable highly autonomous and efficient aircraft navigation. Secondly, the application of AI in image recognition and computer vision is discussed, highlighting its significance in remote sensing and aerospace component quality inspection. The third section examines the integration of AI in unmanned aerial vehicles (UAV), covering the control system and the utilization of machine learning techniques for improved UAV capabilities. Lastly, the paper explores the impact of AI on data analysis and prediction in the aerospace industry, encompassing weather forecasting, resource allocation, and decision-making processes. Finally, this paper gives a general overview of the nowadays application of AI in aerospace engineering.
Keywords
artificial intelligence, aerospace engineering, autonomous navigation, unmanned aerial vehicles
[1]. Izzo, Dario, Marcus Märtens, and Binfeng Pan. "A survey on artificial intelligence trends in spacecraft guidance dynamics and control." Astrodynamics 2019, 3: 287-299.
[2]. Vasile, M., Minisci, E., Locatelli, M. Analysis of some global optimization algorithms for space trajectory design. Journal of Spacecraft and Rockets, 2010, 47(2): 334–344.
[3]. Englander, J. A., Conway, B. A., Williams, T. Automated mission planning via evolutionary algorithms. Journal of Guidance, Control, and Dynamics, 2012, 35(6): 1878–1887.
[4]. Vasile, M., Ricciardi, L. A direct memetic approach to the solution of multi-objective optimal control problems. Proceedings of 2016 IEEE Symposium Series on Computational Intelligence, 2016, 1–8.
[5]. Emami, Seyyed Ali, Paolo Castaldi, and Afshin Banazadeh. "Neural network-based flight control systems: Present and future." Annual Reviews in Control, 2022, 53: 97-137.
[6]. Lary, David John. "Artificial intelligence in geoscience and remote sensing." Geoscience and Remote Sensing New Achievements. IntechOpen, 2010, 1-9.
[7]. Wu Yingnian, Xie Jianwen, Lu Yang, et al. Sparse and Deep Generalizations of the FRAME Model. Annals of Mathematical Sciences and Applications, 2018, 3(1):1-9
[8]. Wang, Lizhe, et al. "Knowledge discovery from remote sensing images: A review." International journal of remote sensing, 2012, 33.13: 4057-4082.
[9]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2020,10.5: e1371.
[10]. Beltrán-González, Carlos, Matteo Bustreo, and Alessio Del Bue. "External and internal quality inspection of aerospace components." 2020 IEEE 7th international workshop on metrology for aerospace, 1-12.
[11]. Xiao, Yuling, and Haoran Zhang. "Research on surface crack detection technology based on digital image processing." Journal of Physics: Conference Series. 2020, 1550. 3.
[12]. Shokirov, Rakhimjon, et al. "Prospects of the development of unmanned aerial vehicles (UAVs)." Technical science and innovation, 2020.3: 4-8.
[13]. Q. Zhang, M. Mozaffari, W. Saad, M. Bennis and M. Debbah, "Machine learning for predictive on-demand deployment of UAVs for wireless communications", Proc. IEEE Global Commun. Conf., 2018, 1-9.
[14]. I. Bozcan and E. Kayacan, UAV-AdNet: Unsupervised anomaly detection using deep neural networks for aerial surveillance, 2020, 1-13.
[15]. C. Titouna, F. Nait-Abdesselam and H. Moungla, "An online anomaly detection approach for unmanned aerial vehicles", Proc. Int. Wireless Commun. Mobile Comput. 2019. 469-474
[16]. EarthRisk Technologies, 2013: TempRisk Apollo White Paper. Available at http://www.earthrisktech.com/resources/reports/white_papers/TempRiskApollo_WhitePaper_Oct2013.pdf. Accessed on 16 August 2019.
Cite this article
Li,W. (2024). The application of artificial intelligence in aerospace engineering. Applied and Computational Engineering,35,17-25.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).