通过融合频域与时域信息，实现海上晃动平台的高精度无人机着陆预测控制。
SpecFuse: A Spectral-Temporal Fusion Predictive Control Framework for UAV Landing on Oscillating Marine Platforms
- 结合波浪频谱分解与递归状态估计，实时预测水面平台六自由度运动。
- 在真实湖面实验中实现4.46厘米着陆偏差、87.5%成功率，延迟仅82毫秒。
- 适合海上搜救、环境监测等需要精准无人艇着陆的场景。
无人飞行器（UAV）在晃动海面平台上的自主着陆受到波浪引起的多频振动、风扰动及运动预测相位滞后严重制约。现有方法或把平台运动视为通用随机过程，或缺乏对波浪频谱特征的显式建模，导致动态海况下性能不佳。为此，我们提出SpecFuse：一种新型频-时域融合预测控制框架，将频域波浪分解与时域递归状态估计相结合，实现无人水面艇（USV）六自由度运动的高精度预测。该框架显式建模主导波浪谐波以消除相位滞后，利用惯性测量单元（IMU）数据实时优化预测，无需复杂标定。同时设计分层控制架构：采用基于采样的HPO-RRT*算法解决非凸约束下的动态轨迹规划；引入学习增强型预测控制器，融合数据驱动的扰动补偿与优化执行。大规模验证（2000次仿真+8次湖上实验）表明，本方法预测误差仅3.2厘米，着陆偏差4.46厘米，仿真/实测成功率分别为98.7%和87.5%，嵌入式硬件延迟为82毫秒，相比最先进方法精度提升44%-48%。其对波浪-风耦合干扰的鲁棒性支持搜救、环境监测等关键海事任务。所有代码、实验配置与数据集将开源，保障可复现性。
原文摘要 · Abstract (English)
Autonomous landing of Uncrewed Aerial Vehicles (UAVs) on oscillating marine platforms is severely constrained by wave-induced multi-frequency oscillations, wind disturbances, and prediction phase lags in motion prediction. Existing methods either treat platform motion as a general random process or lack explicit modeling of wave spectral characteristics, leading to suboptimal performance under dynamic sea conditions. To address these limitations, we propose SpecFuse: a novel spectral-temporal fusion predictive control framework that integrates frequency-domain wave decomposition with time-domain recursive state estimation for high-precision 6-DoF motion forecasting of Uncrewed Surface Vehicles (USVs). The framework explicitly models dominant wave harmonics to mitigate phase lags, refining predictions in real time via IMU data without relying on complex calibration. Additionally, we design a hierarchical control architecture featuring a sampling-based HPO-RRT* algorithm for dynamic trajectory planning under non-convex constraints and a learning-augmented predictive controller that fuses data-driven disturbance compensation with optimization-based execution. Extensive validations (2,000 simulations + 8 lake experiments) show our approach achieves a 3.2 cm prediction error, 4.46 cm landing deviation, 98.7% / 87.5% success rates (simulation / real-world), and 82 ms latency on embedded hardware, outperforming state-of-the-art methods by 44%-48% in accuracy. Its robustness to wave-wind coupling disturbances supports critical maritime missions such as search and rescue and environmental monitoring. All code, experimental configurations, and datasets will be released as open-source to facilitate reproducibility.
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