VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction
This paper addresses the challenge of predicting 3D trajectories for Unmanned Aerial Vehicles (UAVs) in real-time, a critical task for applications like aerial surveillance and defense. Current prediction models primarily leverage only position data, which may not provide the most accurate forecasts...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/8 |
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| author | Omer Nacar Mohamed Abdelkader Lahouari Ghouti Kahled Gabr Abdulrahman Al-Batati Anis Koubaa |
| author_facet | Omer Nacar Mohamed Abdelkader Lahouari Ghouti Kahled Gabr Abdulrahman Al-Batati Anis Koubaa |
| author_sort | Omer Nacar |
| collection | DOAJ |
| description | This paper addresses the challenge of predicting 3D trajectories for Unmanned Aerial Vehicles (UAVs) in real-time, a critical task for applications like aerial surveillance and defense. Current prediction models primarily leverage only position data, which may not provide the most accurate forecasts for UAV movements and usually fail outside the position domain used in the training phase. Our research identifies a gap in utilizing velocity estimates and first-order dynamics to better capture the dynamics and enhance prediction accuracy and generalizability in any position domain. To bridge this gap, we introduce a trajectory prediction scheme using sequence-based neural networks with Gated Recurrent Units (GRUs) to forecast future velocity and positions based on historical velocity estimates instead of position measurements. This approach is designed to improve the predictive capabilities over traditional methods that rely solely on recurrent neural networks (RNNs) or transformers, which can struggle with scalability in this context. Our methodology employs both synthetic and real-world 3D UAV trajectory data, incorporating diverse patterns of agility, curvature, and speed. Synthetic data are generated using the Gazebo robotics simulator and PX4 Autopilot, while real-world data are sourced from the UZH-FPV and Mid-Air drone racing datasets. We train the GRU-based models on drone 3D position and velocity samples to capture the dynamics of UAV movements effectively. Quantitatively, the proposed GRU-based prediction algorithm demonstrates superior performance, achieving a mean square error (MSE) ranging from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>8</mn></mrow></msup></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>7</mn></mrow></msup></mrow></semantics></math></inline-formula>. This performance outstrips existing state-of-the-art RNN models. Overall, our findings confirm the effectiveness of incorporating velocity data in improving the accuracy of UAV trajectory predictions across both synthetic and real-world scenarios, in and out of position data distributions. Finally, we open-source our 5000 trajectories dataset and a ROS2 package to facilitate the integration with existing ROS-based UAV systems. |
| format | Article |
| id | doaj-art-78913aacead54883996af96f26304e2d |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-78913aacead54883996af96f26304e2d2025-01-24T13:29:37ZengMDPI AGDrones2504-446X2024-12-0191810.3390/drones9010008VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory PredictionOmer Nacar0Mohamed Abdelkader1Lahouari Ghouti2Kahled Gabr3Abdulrahman Al-Batati4Anis Koubaa5Robotics and Internet-of-Things Lab, Prince Sultan University, Riyadh 12435, Saudi ArabiaRobotics and Internet-of-Things Lab, Prince Sultan University, Riyadh 12435, Saudi ArabiaRobotics and Internet-of-Things Lab, Prince Sultan University, Riyadh 12435, Saudi ArabiaRobotics and Internet-of-Things Lab, Prince Sultan University, Riyadh 12435, Saudi ArabiaRobotics and Internet-of-Things Lab, Prince Sultan University, Riyadh 12435, Saudi ArabiaRobotics and Internet-of-Things Lab, Prince Sultan University, Riyadh 12435, Saudi ArabiaThis paper addresses the challenge of predicting 3D trajectories for Unmanned Aerial Vehicles (UAVs) in real-time, a critical task for applications like aerial surveillance and defense. Current prediction models primarily leverage only position data, which may not provide the most accurate forecasts for UAV movements and usually fail outside the position domain used in the training phase. Our research identifies a gap in utilizing velocity estimates and first-order dynamics to better capture the dynamics and enhance prediction accuracy and generalizability in any position domain. To bridge this gap, we introduce a trajectory prediction scheme using sequence-based neural networks with Gated Recurrent Units (GRUs) to forecast future velocity and positions based on historical velocity estimates instead of position measurements. This approach is designed to improve the predictive capabilities over traditional methods that rely solely on recurrent neural networks (RNNs) or transformers, which can struggle with scalability in this context. Our methodology employs both synthetic and real-world 3D UAV trajectory data, incorporating diverse patterns of agility, curvature, and speed. Synthetic data are generated using the Gazebo robotics simulator and PX4 Autopilot, while real-world data are sourced from the UZH-FPV and Mid-Air drone racing datasets. We train the GRU-based models on drone 3D position and velocity samples to capture the dynamics of UAV movements effectively. Quantitatively, the proposed GRU-based prediction algorithm demonstrates superior performance, achieving a mean square error (MSE) ranging from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>8</mn></mrow></msup></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>7</mn></mrow></msup></mrow></semantics></math></inline-formula>. This performance outstrips existing state-of-the-art RNN models. Overall, our findings confirm the effectiveness of incorporating velocity data in improving the accuracy of UAV trajectory predictions across both synthetic and real-world scenarios, in and out of position data distributions. Finally, we open-source our 5000 trajectories dataset and a ROS2 package to facilitate the integration with existing ROS-based UAV systems.https://www.mdpi.com/2504-446X/9/1/8unmanned aerial vehicles (UAV)3D trajectory predictionsequence-based neural networksgated recurrent units (GRUs)transformersshort long term memory (LSTM) |
| spellingShingle | Omer Nacar Mohamed Abdelkader Lahouari Ghouti Kahled Gabr Abdulrahman Al-Batati Anis Koubaa VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction Drones unmanned aerial vehicles (UAV) 3D trajectory prediction sequence-based neural networks gated recurrent units (GRUs) transformers short long term memory (LSTM) |
| title | VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction |
| title_full | VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction |
| title_fullStr | VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction |
| title_full_unstemmed | VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction |
| title_short | VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction |
| title_sort | vector velocity enhanced gru neural network for real time 3d uav trajectory prediction |
| topic | unmanned aerial vehicles (UAV) 3D trajectory prediction sequence-based neural networks gated recurrent units (GRUs) transformers short long term memory (LSTM) |
| url | https://www.mdpi.com/2504-446X/9/1/8 |
| work_keys_str_mv | AT omernacar vectorvelocityenhancedgruneuralnetworkforrealtime3duavtrajectoryprediction AT mohamedabdelkader vectorvelocityenhancedgruneuralnetworkforrealtime3duavtrajectoryprediction AT lahouarighouti vectorvelocityenhancedgruneuralnetworkforrealtime3duavtrajectoryprediction AT kahledgabr vectorvelocityenhancedgruneuralnetworkforrealtime3duavtrajectoryprediction AT abdulrahmanalbatati vectorvelocityenhancedgruneuralnetworkforrealtime3duavtrajectoryprediction AT aniskoubaa vectorvelocityenhancedgruneuralnetworkforrealtime3duavtrajectoryprediction |