基于扩散的无人机集群四维轨迹预测与分布式控制
Diffusion-based 4D Trajectory Prediction and Distributed Control for UAV Swarms
AI总结：
提出统一框架，结合粗到细轨迹预测与不确定性感知分布式非线性模型预测控制，通过维度解耦和扩散残差动力学模块降低计算复杂度并捕捉动态不确定性，在复杂环境下实现亚0.07米平均跟踪误差和34 FPS实时推理。
摘要：
精确的四维轨迹预测和闭环跟踪对于无人机集群在城市空域、工业场地和室内设施等复杂低空环境中实现安全高效运行至关重要。然而，由于无人机集群动力学的内在非线性和集群编队控制的严格实时约束，这一任务仍然具有挑战性。为了解决这些问题，我们提出了一个统一框架，将粗到细轨迹预测与不确定性感知分布式非线性模型预测控制（DNMPC）相结合。我们的方法具有两个关键创新：1）维度解耦轨迹预测模块，通过预测轴运动降低计算复杂度；2）基于扩散的残差动力学细化模块，捕捉时间相关的动态不确定性。这些细化的预测随后被集成到DNMPC循环中，以确保编队稳定性。我们还引入了一个同步的多场景四维无人机集群数据集，涵盖六个代表性空域场景。该数据集包含超过7,900帧同步的三无人机轨迹，并带有帧级的速度意图和目标扇区标注。大量实验表明，我们的方法优于最先进的基线，在复杂城市和工业环境中将轨迹跟踪误差降低高达10-15%，实现亚0.07米的平均跟踪误差，同时保持34 FPS（低于30毫秒延迟）的实时推理速度，适用于敏捷飞行。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
