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

2026-02-17

AI Summary

This study addresses the challenge of achieving high-precision autonomous landing of unmanned aerial vehicles on oscillating maritime platforms under multi-frequency wave-induced motion, wind disturbances, and phase-lag effects in prediction. The authors propose a six-degree-of-freedom motion prediction method that integrates explicit frequency-domain harmonic modeling of wave dynamics with time-domain recursive state estimation, enabling real-time IMU-based prediction without complex calibration. A novel hierarchical control architecture is introduced: an upper layer employs HPO-RRT* for dynamic trajectory planning, while a lower layer leverages learning-augmented predictive control for disturbance compensation. Experimental results demonstrate that the system achieves a low latency of 82 ms on embedded hardware, with a prediction error of only 3.2 cm and a landing deviation of 4.46 cm. Simulation and real-world landing success rates reach 98.7% and 87.5%, respectively, representing a 44%–48% improvement in accuracy over existing methods.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• UAV landing
• oscillating marine platforms
• wave-induced oscillations
• motion prediction
• phase lag

Innovation

• spectral-temporal fusion
• wave harmonic modeling
• predictive control
• HPO-RRT*
• learning-augmented control