Abstract
This work addresses the challenge of autonomous Unmanned Aerial Vehicle (UAV) navigation in Global Positioning System (GPS)-denied environments by proposing a new approach that is an amalgamation of data-driven and model-based philosophies. The proposed method exploits datasets acquired from existing frameworks like the Generalized Trust Region Sub-problem (GTRS) and the Weighted Least Squares (WLS). These datasets are then used to feed the proposed Convolutional Neural Network (CNN) specially tailored to create models for UAV navigation. Afterwards, these models are used to make predictions of an optimal trajectory. The obtained numerical results reveal that the proposed CNN reveals improvements in accuracy and robustness to noise when compared to other Machine Learning approaches, while reducing the required training time.
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References
Innocenti, E., Agostini, G., Giuliano, R.: UAVs for medicine delivery in a smart city using fiducial markers. Information 13(10) (2022)
Máthé, K., Buşoniu, L.: Vision and control for UAVs: a survey of general methods and of inexpensive platforms for infrastructure inspection. Sensors 15(7), 14887–14916 (2015)
Matos-Carvalho, J.P., et al.: Static and dynamic algorithms for terrain classification in UAV aerial imagery. Remote Sens. 11(21), 2501 (2019)
Tomic, T., et al.: Toward a fully autonomous UAV: research platform for indoor and outdoor urban search and rescue. IEEE Robot. Autom. Mag. 19(3), 46–56 (2012)
Khan, S.K., Naseem, U., Siraj, H., Razzak, I., Imran, M.: The role of unmanned aerial vehicles and mmWave in 5G: recent advances and challenges. Trans. Emerg. Telecommun. Technol. 32(7), e4241 (2021)
Correia, S.D., Fé, J., Tomic, S., Beko, M.: Drones as sound sensors for energy-based acoustic tracking on wildfire environments. In: Camarinha-Matos, L.M., Heijenk, G., Katkoori, S., Strous, L. (eds.) Internet of Things. Technology and Applications, pp. 109–125. Springer International Publishing (2022). https://doi.org/10.1007/978-3-030-96466-5_8
Salvado, A.B., et al.: Semantic navigation mapping from aerial multispectral imagery. In: 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), pp. 1192–1197 (2019)
Pedro, D., Mora, A., Carvalho, J., Azevedo, F., Fonseca, J.: ColANet: A UAV collision avoidance dataset. In: Camarinha-Matos, L.M., Farhadi, N., Lopes, F., Pereira, H. (eds.) DoCEIS 2020. IAICT, vol. 577, pp. 53–62. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45124-0_5
Shen, Y., Hwang, B., Jeong, J.P.: Particle filtering-based indoor positioning system for beacon tag tracking. IEEE Access 8, 226445–226460 (2020)
Matos-Carvalho, J.P., Santos, R., Tomic, S., Beko, M.: GTRS-based algorithm for UAV navigation in indoor environments employing range measurements and odometry. IEEE Access 9, 89120–89132 (2021)
Santos, R., Matos-Carvalho, J.P., Tomic, S., Beko, M.: Wls algorithm for UAV navigation in satellite-less environments. IET Wirel. Sens. Syst. 12(3), 93–102 (2022)
Santos, R., Matos-Carvalho, J.P., Tomic, S., Beko, M., Correia, S.D.: Applying deep neural networks to improve UAV navigation in satellite-less environments. In: 2022 International Young Engineers Forum (YEF-ECE), pp. 63–68 (2022)
Yang, B., Li, J., Shao, Z., Zhang, H.: Robust UWB indoor localization for NLoS scenes via learning spatial-temporal features. IEEE Sens. J. 22(8), 7990–8000 (2022)
Rappaport, T.S.: Wireless Communications – Principles and Practice. Prentice Hall (1996)
Tomic, S., Beko, M., Dinis, R.: RSS-based localization in wireless sensor networks using convex relaxation: noncooperative and cooperative schemes. IEEE Trans. Veh. Technol. 64(5), 2037–2050 (2015)
Comuniello, A., Angelis, A., Moschitta, A., Carbone, P.: Using bluetooth low energy technology to perform ToF-based positioning. Electronics 11, 111 (2021)
Hashem, O., Harras, K., Youssef, M.: Accurate indoor positioning using IEEE 802.11mc round trip time. Pervasive Mob. Comput. 75, 101416 (2021)
Kay, S.M.: Fundamentals of Statistical Signal Processing: Estimation Theory, 1st edn. Prentice Hall, Upper Saddle River (1993)
Tomic, S., Beko, M., Dinis, R.: 3-D target localization in wireless sensor networks using RSS and AoA measurements. IEEE Trans. Veh. Technol. 66(4), 3197–3210 (2017)
Acknowledgment
This research was partially funded by Fundação para a Ciência e a Tecnologia under Projects UIDB/04111/2020, 2021.04180.CEECIND, UIDB/50008/2020, CEECINST/00147/2018/CP1498/CT0015 and ROBUST EXPL/EEIEEE/0776/2021 and by the European Union’s Horizon Europe Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No. 101086387, as well as Instituto Lusófono de Investigação e Desenvolvimento (ILIND) under Project COFAC/ILIND/COPELABS/1/2022.
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Santos, R.S., Matos-Carvalho, J.P., Tomic, S., Beko, M., Calafate, C.T. (2024). Convolutional Neural Networks for Autonomous UAV Navigation in GPS-Denied Environments. In: Camarinha-Matos, L.M., Ferrada, F. (eds) Technological Innovation for Human-Centric Systems. DoCEIS 2024. IFIP Advances in Information and Communication Technology, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-63851-0_7
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