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SpecFuse: A Spectral-Temporal Fusion Predictive Control Framework for UAV Landing on Oscillating Marine Platforms

Haichao Liu, Yufeng Hu, Shuang Wang, Kangjun Guo, Jun Ma, Jinni Zhou

Abstract

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.

SpecFuse: A Spectral-Temporal Fusion Predictive Control Framework for UAV Landing on Oscillating Marine Platforms

Abstract

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.
Paper Structure (23 sections, 22 equations, 5 figures, 4 tables, 1 algorithm)

This paper contains 23 sections, 22 equations, 5 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Real-Time Planning under Dynamic Disturbances
  4. Sensor Fusion for Active Perching
  5. Attitude Prediction under Wave Effects
  6. Methodology
  7. Motion Prediction for Oscillating Marine Platforms
  8. Spectral Decomposition of Wave Motion
  9. Recursive State Estimation
  10. Condition Prediction
  11. SE(3) Trajectory Planning for the UAV
  12. Execution Controller for the UAV
  13. Learning-Based Velocity Matching
  14. Receding Horizon Descent Control
  15. Experimental Results and Analysis
  16. ...and 8 more sections

Figures (5)

  • Figure 1: Overview of the UAV-USV maritime rescue operation. The proposed SpecFuse framework (spectral-temporal fusion + learning-augmented control) enables the UAV to predict the USV’s 6-DoF motion (50Hz) and adjust trajectories dynamically, ensuring precise landing ($<$5cm deviation) for medical resupply missions.
  • Figure 2: Proposed control architecture. The Motion Prediction module forecasts the platform's state, while the Learning-Augmented controller utilizes multi-source feedback to command the UAV actuators.
  • Figure 3: Numerical validation in simulation of adaptive trajectory planning for UAV/USV cooperative landing under high-fidelity marine disturbances. The blue line represents the UAV’s planned trajectory, while the square platform with coordinate axes indicates the pose of the oscillating marine platform.
  • Figure 4: Experimental setup in the outdoor environment. A wave-generating vehicle follows a predefined trajectory around the USV, with the UAV's field of view indicated.
  • Figure 5: Autonomous UAV landing on an oscillating marine platform in an outdoor lake environment. The left column displays the third-person view with the UAV highlighted by red circles, while the right column shows the corresponding onboard downward-facing camera views of the landing target.