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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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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