Diffusion-based 4D Trajectory Prediction and Distributed Control for UAV Swarms

• URL: http://arxiv.org/abs/2606.31197v1
• Date: Tue, 30 Jun 2026 06:26:34 GMT
• Title: Diffusion-based 4D Trajectory Prediction and Distributed Control for UAV Swarms
• Abstract summary: We propose a unified framework that couples coarse-to-fine trajectory forecasting with uncertainty-aware Predictive Model Predictive Control (DNMPC)<n>Our approach features two key innovations: 1) a dimension-decoupled trajectory prediction module that reduces computational complexity by forecasting axis-wise motion, and 2) a diffusion-based residual dynamics refinement module that captures temporally correlated dynamic uncertainties.<n>Our approach outperforms state-of-the-art baselines, reducing trajectory tracking error by up to textbf10-15% and achieving sub-textbf0.07,m average tracking error in complex urban and industrial
• Score: 9.09854701245782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate 4D trajectory prediction and closed-loop tracking are essential for Unmanned Aerial Vehicle (UAV) swarms to achieve safe and efficient operations in complex low-altitude environments such as urban airspaces, industrial sites, and indoor facilities. However, this task remains challenging due to intrinsic nonlinearity of UAV swarm dynamics and strict real-time constraints of swarm formation control. To address these challenges, we propose a unified framework that couples coarse-to-fine trajectory forecasting with uncertainty-aware Distributed Nonlinear Model Predictive Control (DNMPC). Our approach features two key innovations: 1) a dimension-decoupled trajectory prediction module that reduces computational complexity by forecasting axis-wise motion, and 2) a diffusion-based residual dynamics refinement module that captures temporally correlated dynamic uncertainties. These refined predictions are then integrated into a DNMPC loop to ensure formation stability. We also introduce a synchronized multi-scenario 4D UAV swarm dataset spanning six representative airspace scenarios. The dataset contains over \textbf{7,900} frames of synchronized three-UAV trajectories with frame-level annotations of speed intention and target sector. Extensive experiments demonstrate that our approach outperforms state-of-the-art baselines, reducing trajectory tracking error by up to \textbf{10-15\%} and achieving sub-\textbf{0.07\,m} average tracking error in complex urban and industrial environments, while maintaining real-time inference speeds of 34 FPS (sub-30 ms latency) suitable for agile flight.

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