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 2 × 10 8 to 2 × 10 7 . 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.


    Access

    Download


    Export, share and cite



    Title :

    VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction


    Contributors:


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field

    Sormoli, MReza Alipour / Samadi, Amir / Mozaffari, Sajjad et al. | IEEE | 2023


    Real-time Short- Term Trajectory Prediction Based on GRU Neural Network

    Han, Ping / Wang, Wenqing / Shi, Qingyan et al. | IEEE | 2019


    NEURAL NETWORK TRAJECTORY PREDICTION

    CHEN YUXIAO / IVANOVIC BORIS / PAVONE MARCO | European Patent Office | 2023

    Free access

    Real-Time Targeting and Trajectory Estimation for Enhanced Network Centricity

    Coleman, N. / Lam, K. / Patel, K. et al. | British Library Conference Proceedings | 2008


    Real-Time Targeting and Trajectory Estimation for Enhanced Network Centricity

    Coleman, Norman / Lam, Ken / Patel, Ketula et al. | AIAA | 2008