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Year 2024, Volume: 8 Issue: 2, 81 - 85

Abstract

References

  • [1] Shao, Y., & Yang, S. (2020). Applications of Generative Adversarial Networks (GANs) in Aircraft Design: A Survey. Aerospace, 7(8), 108. https://doi.org/10.3390/aerospace7080108
  • [2] Zhou, D., Hu, H., Wu, J., & Peng, Z. (2020). Generative adversarial networks in UAV-aided communications: A comprehensive survey. IEEE Access, 8, 135857-135876. https://doi.org/10.1109/ACCESS.2020.3015019
  • [3] Karaman, S., & Frazzoli, E. (2020). Autonomous aerial vehicles: A survey of the state of the art. Proceedings of the IEEE, 108(2), 252-294. https://doi.org/10.1109/JPROC.2019.2955685
  • [4] Brown, C., De Wet, P., Bruckstein, A. M., & Sukkarieh, S. (2020). A survey of visual SLAM in unmanned aerial vehicles. Journal of Field Robotics, 37(3), 405-448. https://doi.org/10.1002/rob.21892
  • [5] Khalifa, S., Mustafa, M., & Guizani, M. (2021). Machine Learning-Driven Autonomous UAV Navigation: Challenges and Opportunities. IEEE Access, 9, 73138-73156. https://doi.org/10.1109/ACCESS.2021.3073353
  • [6] Yang, L., Cui, W., & Meng, X. (2021). A Survey of Key Technologies for Autonomous Flight of UAVs. IEEE Access, 9, 68745-68766. https://doi.org/10.1109/ACCESS.2021.3070011
  • [7] Zhang, Y., Chen, L., & Zhang, H. (2020). A survey on deep learning for UAV-based communication networks. IEEE Access, 8, 22147-22159. https://doi.org/10.1109/ACCESS.2020.2960668
  • [8] Sun, Y., Yu, L., & Zhang, Y. (2021). A Survey of Machine Learning for Unmanned Aerial Vehicles: Recent Advances, Taxonomy, and Challenges. IEEE Transactions on Intelligent Transportation Systems, 22(1), 543-562. https://doi.org/10.1109/TITS.2020.2979192
  • [9] Roldán-Álvarez, D., Del-Blanco, C. R., & Cuadra-Troncoso, A. (2020). UAV-based data collection, processing, and analysis for smart agriculture: A comprehensive review of advances and applications. Computers and Electronics in Agriculture, 178, 105766. https://doi.org/10.1016/j.compag.2020.105766
  • [10] Jia, F., Dong, Y., Shi, H., Wang, J., & Chen, L. (2021). Recent advances in machine learning for unmanned aerial vehicles: Algorithms, tools, and applications. Journal of Aerospace Information Systems, 18(1), 32-49. https://doi.org/10.2514/1.I010984

Autonomous Flight Systems and Generative AI

Year 2024, Volume: 8 Issue: 2, 81 - 85

Abstract

Autonomous flight systems have emerged as a significant area of research and development within the aviation industry. With the advancements in artificial intelligence (AI), particularly generative AI, these systems have witnessed substantial improvements in their capabilities and efficiency. This abstract explores the integration of generative AI techniques in autonomous flight systems and its implications on the aviation sector. Generative AI algorithms play a crucial role in various aspects of autonomous flight, including flight path planning, obstacle detection and avoidance, decision-making processes, and even aircraft design optimization. By leveraging generative AI, autonomous flight systems can adapt and respond to dynamic environments in real-time, enhancing safety, efficiency, and reliability. Furthermore, generative AI enables the generation of innovative solutions and designs that may not be apparent through traditional methods, leading to more optimized and efficient aircraft configurations. This abstract also discusses the challenges and future directions in the utilization of generative AI in autonomous flight systems, including regulatory considerations, ethical concerns, and the need for continued research and development. Overall, the integration of generative AI in autonomous flight systems represents a promising avenue for advancing the capabilities and effectiveness of aviation technology in the 21st century.

Supporting Institution

Aselsan-Bites

References

  • [1] Shao, Y., & Yang, S. (2020). Applications of Generative Adversarial Networks (GANs) in Aircraft Design: A Survey. Aerospace, 7(8), 108. https://doi.org/10.3390/aerospace7080108
  • [2] Zhou, D., Hu, H., Wu, J., & Peng, Z. (2020). Generative adversarial networks in UAV-aided communications: A comprehensive survey. IEEE Access, 8, 135857-135876. https://doi.org/10.1109/ACCESS.2020.3015019
  • [3] Karaman, S., & Frazzoli, E. (2020). Autonomous aerial vehicles: A survey of the state of the art. Proceedings of the IEEE, 108(2), 252-294. https://doi.org/10.1109/JPROC.2019.2955685
  • [4] Brown, C., De Wet, P., Bruckstein, A. M., & Sukkarieh, S. (2020). A survey of visual SLAM in unmanned aerial vehicles. Journal of Field Robotics, 37(3), 405-448. https://doi.org/10.1002/rob.21892
  • [5] Khalifa, S., Mustafa, M., & Guizani, M. (2021). Machine Learning-Driven Autonomous UAV Navigation: Challenges and Opportunities. IEEE Access, 9, 73138-73156. https://doi.org/10.1109/ACCESS.2021.3073353
  • [6] Yang, L., Cui, W., & Meng, X. (2021). A Survey of Key Technologies for Autonomous Flight of UAVs. IEEE Access, 9, 68745-68766. https://doi.org/10.1109/ACCESS.2021.3070011
  • [7] Zhang, Y., Chen, L., & Zhang, H. (2020). A survey on deep learning for UAV-based communication networks. IEEE Access, 8, 22147-22159. https://doi.org/10.1109/ACCESS.2020.2960668
  • [8] Sun, Y., Yu, L., & Zhang, Y. (2021). A Survey of Machine Learning for Unmanned Aerial Vehicles: Recent Advances, Taxonomy, and Challenges. IEEE Transactions on Intelligent Transportation Systems, 22(1), 543-562. https://doi.org/10.1109/TITS.2020.2979192
  • [9] Roldán-Álvarez, D., Del-Blanco, C. R., & Cuadra-Troncoso, A. (2020). UAV-based data collection, processing, and analysis for smart agriculture: A comprehensive review of advances and applications. Computers and Electronics in Agriculture, 178, 105766. https://doi.org/10.1016/j.compag.2020.105766
  • [10] Jia, F., Dong, Y., Shi, H., Wang, J., & Chen, L. (2021). Recent advances in machine learning for unmanned aerial vehicles: Algorithms, tools, and applications. Journal of Aerospace Information Systems, 18(1), 32-49. https://doi.org/10.2514/1.I010984
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Details

Primary Language English
Subjects Artificial Intelligence (Other)
Journal Section Articles
Authors

Ali Berkol 0000-0003-1704-1581

İdil Gökçe Demirtaş This is me 0009-0000-4614-5211

Early Pub Date December 11, 2024
Publication Date
Submission Date November 11, 2024
Acceptance Date December 9, 2024
Published in Issue Year 2024 Volume: 8 Issue: 2

Cite

IEEE A. Berkol and İ. G. Demirtaş, “Autonomous Flight Systems and Generative AI”, IJMSIT, vol. 8, no. 2, pp. 81–85, 2024.