Autonomous Real-Time Smoothness Control for Reliable DDQN-Based UAV Navigation Using Cellular Networks
Reliable Unmanned Aerial Vehicle (UAV) navigation in urban environments is a crucial prerequisite for major civilian and military applications. Many existing Global Positioning System (GPS)-based UAV navigation solutions do not meet the performance requirements given their unreliability in urban env...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10847807/ |
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| author | Ghada Afifi Yasser Gadallah |
| author_facet | Ghada Afifi Yasser Gadallah |
| author_sort | Ghada Afifi |
| collection | DOAJ |
| description | Reliable Unmanned Aerial Vehicle (UAV) navigation in urban environments is a crucial prerequisite for major civilian and military applications. Many existing Global Positioning System (GPS)-based UAV navigation solutions do not meet the performance requirements given their unreliability in urban environments. In this paper, we present a smooth trajectory planning approach to generate reliable UAV trajectories with less chatter and sharp turns. We propose to utilize broadcast signals from existing cellular networks to practically navigate the UAV from a given source to a destination in urban environments independent of GPS or other transmissible signals. For this purpose, we formulate the smooth trajectory planning problem as an optimization problem to provide a probabilistic guarantee on the success of the UAV mission considering the UAV dynamic and kinematic constraints. We utilize proper optimization-based techniques to determine the optimal bound of the solution for benchmarking purposes. Next, we propose a machine learning based approach to provide a practical real-time solution to the formulated UAV navigation problem. Finally, we present an in-depth comparative analysis to evaluate the performance of the proposed double deep Q-network (DDQN)-based technique as compared to other solutions from the literature. |
| format | Article |
| id | doaj-art-055dc5360b834a8a837a35e9b5eb2e1b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-055dc5360b834a8a837a35e9b5eb2e1b2025-02-06T00:00:49ZengIEEEIEEE Access2169-35362025-01-0113220112202810.1109/ACCESS.2025.353193110847807Autonomous Real-Time Smoothness Control for Reliable DDQN-Based UAV Navigation Using Cellular NetworksGhada Afifi0https://orcid.org/0000-0002-2136-3586Yasser Gadallah1https://orcid.org/0000-0001-8099-505XElectronics and Communications Engineering Department, The American University in Cairo, New Cairo, EgyptElectronics and Communications Engineering Department, The American University in Cairo, New Cairo, EgyptReliable Unmanned Aerial Vehicle (UAV) navigation in urban environments is a crucial prerequisite for major civilian and military applications. Many existing Global Positioning System (GPS)-based UAV navigation solutions do not meet the performance requirements given their unreliability in urban environments. In this paper, we present a smooth trajectory planning approach to generate reliable UAV trajectories with less chatter and sharp turns. We propose to utilize broadcast signals from existing cellular networks to practically navigate the UAV from a given source to a destination in urban environments independent of GPS or other transmissible signals. For this purpose, we formulate the smooth trajectory planning problem as an optimization problem to provide a probabilistic guarantee on the success of the UAV mission considering the UAV dynamic and kinematic constraints. We utilize proper optimization-based techniques to determine the optimal bound of the solution for benchmarking purposes. Next, we propose a machine learning based approach to provide a practical real-time solution to the formulated UAV navigation problem. Finally, we present an in-depth comparative analysis to evaluate the performance of the proposed double deep Q-network (DDQN)-based technique as compared to other solutions from the literature.https://ieeexplore.ieee.org/document/10847807/UAVtrajectory planningcellular networksreliabilitysmoothness controlmachine learning |
| spellingShingle | Ghada Afifi Yasser Gadallah Autonomous Real-Time Smoothness Control for Reliable DDQN-Based UAV Navigation Using Cellular Networks IEEE Access UAV trajectory planning cellular networks reliability smoothness control machine learning |
| title | Autonomous Real-Time Smoothness Control for Reliable DDQN-Based UAV Navigation Using Cellular Networks |
| title_full | Autonomous Real-Time Smoothness Control for Reliable DDQN-Based UAV Navigation Using Cellular Networks |
| title_fullStr | Autonomous Real-Time Smoothness Control for Reliable DDQN-Based UAV Navigation Using Cellular Networks |
| title_full_unstemmed | Autonomous Real-Time Smoothness Control for Reliable DDQN-Based UAV Navigation Using Cellular Networks |
| title_short | Autonomous Real-Time Smoothness Control for Reliable DDQN-Based UAV Navigation Using Cellular Networks |
| title_sort | autonomous real time smoothness control for reliable ddqn based uav navigation using cellular networks |
| topic | UAV trajectory planning cellular networks reliability smoothness control machine learning |
| url | https://ieeexplore.ieee.org/document/10847807/ |
| work_keys_str_mv | AT ghadaafifi autonomousrealtimesmoothnesscontrolforreliableddqnbaseduavnavigationusingcellularnetworks AT yassergadallah autonomousrealtimesmoothnesscontrolforreliableddqnbaseduavnavigationusingcellularnetworks |