Recursive neural network-based design of unmanned aircraft swarm collaborative mission execution and autonomous navigation system

[1] Caterini, A. L., Chang, D. E., Caterini, A. L., & Chang, D. E. (2018). Recurrent neural networks. Deep neural networks in a mathematical framework, 59-79. CateriniA. L.ChangD. E.CateriniA. L. & ChangD. E. (2018). Recurrent neural networks. Deep neural networks in a mathematical framework, 59-79.Search in Google Scholar

[2] Tarwani, K. M., & Edem, S. (2017). Survey on recurrent neural network in natural language processing. Int. J. Eng. Trends Technol, 48(6), 301-304. TarwaniK. M. & EdemS. (2017). Survey on recurrent neural network in natural language processing. Int. J. Eng. Trends Technol, 48(6), 301-304.Search in Google Scholar

[3] Orojo, O., Tepper, J., McGinnity, T. M., & Mahmud, M. (2023). The Multi-Recurrent Neural Network for State-Of-The-Art Time-Series Processing. Procedia Computer Science, 222, 488-498. OrojoO.TepperJ.McGinnityT. M. & MahmudM. (2023). The Multi-Recurrent Neural Network for State-Of-The-Art Time-Series Processing. Procedia Computer Science, 222, 488-498.Search in Google Scholar

[4] Santoro, A., Faulkner, R., Raposo, D., Rae, J., Chrzanowski, M., Weber, T., ... & Lillicrap, T. (2018). Relational recurrent neural networks. Advances in neural information processing systems, 31. SantoroA.FaulknerR.RaposoD.RaeJ.ChrzanowskiM.WeberT. ... & LillicrapT. (2018). Relational recurrent neural networks. Advances in neural information processing systems, 31.Search in Google Scholar

[5] Wang, Z., Lin, J., & Wang, Z. (2017). Accelerating recurrent neural networks: A memory-efficient approach. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 25(10), 2763-2775. WangZ.LinJ. & WangZ. (2017). Accelerating recurrent neural networks: A memory-efficient approach. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 25(10), 2763-2775.Search in Google Scholar

[6] Frady, E. P., Kleyko, D., & Sommer, F. T. (2018). A theory of sequence indexing and working memory in recurrent neural networks. Neural Computation, 30(6), 1449-1513. FradyE. P.KleykoD. & SommerF. T. (2018). A theory of sequence indexing and working memory in recurrent neural networks. Neural Computation, 30(6), 1449-1513.Search in Google Scholar

[7] Laghari, A. A., Jumani, A. K., Laghari, R. A., & Nawaz, H. (2023). Unmanned aerial vehicles: A review. Cognitive Robotics, 3, 8-22. LaghariA. A.JumaniA. K.LaghariR. A. & NawazH. (2023). Unmanned aerial vehicles: A review. Cognitive Robotics, 3, 8-22.Search in Google Scholar

[8] Zeng, Y., Zhang, R., & Lim, T. J. (2016). Wireless communications with unmanned aerial vehicles: Opportunities and challenges. IEEE Communications magazine, 54(5), 36-42. ZengY.ZhangR. & LimT. J. (2016). Wireless communications with unmanned aerial vehicles: Opportunities and challenges. IEEE Communications magazine, 54(5), 36-42.Search in Google Scholar

[9] Zuo, Z., Liu, C., Han, Q. L., & Song, J. (2022). Unmanned aerial vehicles: Control methods and future challenges. IEEE/CAA Journal of Automatica Sinica, 9(4), 601-614. ZuoZ.LiuC.HanQ. L. & SongJ. (2022). Unmanned aerial vehicles: Control methods and future challenges. IEEE/CAA Journal of Automatica Sinica, 9(4), 601-614.Search in Google Scholar

[10] Ahmed, F., Mohanta, J. C., Keshari, A., & Yadav, P. S. (2022). Recent advances in unmanned aerial vehicles: a review. Arabian Journal for Science and Engineering, 47(7), 7963-7984. AhmedF.MohantaJ. C.KeshariA. & YadavP. S. (2022). Recent advances in unmanned aerial vehicles: a review. Arabian Journal for Science and Engineering, 47(7), 7963-7984.Search in Google Scholar

[11] Bijjahalli, S., Sabatini, R., & Gardi, A. (2020). Advances in intelligent and autonomous navigation systems for small UAS. Progress in Aerospace Sciences, 115, 100617. BijjahalliS.SabatiniR. & GardiA. (2020). Advances in intelligent and autonomous navigation systems for small UAS. Progress in Aerospace Sciences, 115, 100617.Search in Google Scholar

[12] Al-Kaff, A., Armingol, J. M., & de La Escalera, A. (2019). A vision-based navigation system for Unmanned Aerial Vehicles (UAVs). Integrated Computer-Aided Engineering, 26(3), 297-310. Al-KaffA.ArmingolJ. M. & de La EscaleraA. (2019). A vision-based navigation system for Unmanned Aerial Vehicles (UAVs). Integrated Computer-Aided Engineering, 26(3), 297-310.Search in Google Scholar

[13] Miranda, V. R., Rezende, A. M., Rocha, T. L., Azpúrua, H., Pimenta, L. C., & Freitas, G. M. (2022). Autonomous navigation system for a delivery drone. Journal of Control, Automation and Electrical Systems, 33, 141-155. MirandaV. R.RezendeA. M.RochaT. L.AzpúruaH.PimentaL. C. & FreitasG. M. (2022). Autonomous navigation system for a delivery drone. Journal of Control, Automation and Electrical Systems, 33, 141-155.Search in Google Scholar

[14] Tang, J., Duan, H., & Lao, S. (2023). Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: A comprehensive review. Artificial Intelligence Review, 56(5), 4295-4327. TangJ.DuanH. & LaoS. (2023). Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: A comprehensive review. Artificial Intelligence Review, 56(5), 4295-4327.Search in Google Scholar

[15] Rezwan, S., & Choi, W. (2022). Artificial intelligence approaches for UAV navigation: Recent advances and future challenges. IEEE access, 10, 26320-26339. RezwanS. & ChoiW. (2022). Artificial intelligence approaches for UAV navigation: Recent advances and future challenges. IEEE access, 10, 26320-26339.Search in Google Scholar

[16] Amer, K., Samy, M., Shaker, M., & ElHelw, M. (2021, January). Deep convolutional neural network based autonomous drone navigation. In Thirteenth International Conference on Machine Vision (Vol. 11605, pp. 16-24). SPIE. AmerK.SamyM.ShakerM. & ElHelwM. (2021, January). Deep convolutional neural network based autonomous drone navigation. In Thirteenth International Conference on Machine Vision (Vol. 11605, pp. 16-24). SPIE.Search in Google Scholar