Prediction-Informed Landing Window Planning for UAV Landing on USV in High Sea States

Kaizhen Li; Xi Zhou; Dan Zhang; Xiaowei Li

cs.ROOct 9, 2026

Prediction-Informed Landing Window Planning for UAV Landing on USV in High Sea States

Authors: Kaizhen Li, Xi Zhou, Dan Zhang, Xiaowei Li

Organizations: School of Mechatronic Engineering and Automation Shanghai University Shanghai, China

Abstract

Autonomous landing of unmanned aerial vehicle (UAV) on unmanned surface vehicle (USV) is difficult in high sea states. Existing hierarchical landing methods often infer future USV motion only implicitly from short observation histories, which limits their ability to adapt to severe and rapidly changing sea conditions. This paper presents a prediction-informed hierarchical control framework. A pretrained model is developed to explicitly predict the USV attitude during high-level landing decisions. The prediction helps identify suitable landing windows and choose the right touchdown time, while a conventional low-level flight controller maintains UAV stability and tracks motion commands. To evaluate the framework under realistic disturbances, a real-sim validation setup is constructed in which USV-motion data collected in real sea conditions are transferred to a MuJoCo USV. The experiments demonstrate the feasibility of using explicit attitude prediction to improve landing-window selection and support timing-aware UAV landing planning in rapidly varying marine environments.

Figures & tables

Fig. 1: Overview of the prediction-informed landing framework. Left: MuJoCo simulation driven by real sea-state USV motion data, where red and green marks indicate dangerous and safe landing windows. Right: the real USV from which roll and pitch data are collected and loaded into the simulation.

Fig. 2: Prediction-informed landing architecture.

Model	Metric
	RMSE	MAE
Informer	1.286	0.841
LSTM	0.913	0.557

TABLE I: Metric of Predicted USV Pitch Angle

Panel of Figure 5 — Fig. 5: Motion segments of the landing policy. Each segment shows the UAV height, the PPO vertical-velocity command and the actual vertical velocity, and the USV deck tilt. The dashed lines mark the contact or safety thresholds.

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

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Aerial Robotics Model Predictive Control Vessel Trajectory Prediction

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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.

Abstract

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