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

2026-06-30

AI Summary

This work addresses the challenges of inaccurate 4D trajectory prediction and unstable formation flight in drone swarms operating in complex low-altitude environments, where nonlinear dynamics and stringent real-time requirements pose significant difficulties. To this end, the authors propose a unified closed-loop control framework that integrates an efficient dimension-decoupled trajectory predictor, a diffusion-model-based residual dynamics refinement module to capture temporal dynamic uncertainties, and an uncertainty-aware distributed nonlinear model predictive controller (DNMPC) for robust formation stabilization. Evaluated on a newly constructed synchronized multi-scenario 4D drone swarm dataset, the method achieves an average tracking error below 0.07 meters in complex urban and industrial settings—outperforming existing approaches by 10–15%—while maintaining a real-time inference rate of 34 FPS (latency < 30 ms).

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.

Problem

• 4D trajectory prediction
• UAV swarms
• nonlinear dynamics
• real-time constraints
• formation control

Innovation

• diffusion-based refinement
• dimension-decoupled prediction
• distributed NMPC
• 4D trajectory prediction
• UAV swarm control