TY - GEN
T1 - Convolutional Neural Networks for Autonomous UAV Navigation in GPS-Denied Environments
AU - Santos, Ricardo Serras
AU - Matos-Carvalho, João P.
AU - Tomic, Slavisa
AU - Beko, Marko
AU - Calafate, Carlos T.
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - 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.
AB - 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.
KW - Convolutional Neural Network (CNN)
KW - Generalized Trust Region Sub-Problem (GTRS)
KW - Navigation
KW - Unmanned Aerial Vehicle (UAV)
KW - Weighted Least Squares (WLS)
UR - http://www.scopus.com/inward/record.url?scp=85199651692&partnerID=8YFLogxK
UR - https://research.ulusofona.pt/en/publications/28b141ed-b402-4313-84c9-4ae80d3be891
U2 - 10.1007/978-3-031-63851-0_7
DO - 10.1007/978-3-031-63851-0_7
M3 - Conference contribution
AN - SCOPUS:85199651692
SN - 9783031638503
T3 - IFIP Advances in Information and Communication Technology
SP - 111
EP - 122
BT - Technological Innovation for Human-Centric Systems - 15th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2024, Proceedings
A2 - Camarinha-Matos, Luis M.
A2 - Ferrada, Filipa
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2024
Y2 - 3 July 2024 through 5 July 2024
ER -