论文/arXiv 2610.13190·19 天前

高海况下UAV在USV上着陆的预测信息辅助着陆窗口规划

针对高海况下UAV在USV上自主降落，提出显式预测USV姿态的分层控制框架，并在真实海况数据驱动的MuJoCo实仿设置中验证。

AI 整理

• 本体 SDK 与控制
• 仿真与训练

原文链接：Prediction-Informed Landing Window Planning for UAV Landing on USV in High Sea States（arXiv 2610.13190 · 发布日期：2026-10-09）

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

针对高海况下UAV在USV上自主降落，提出显式预测USV姿态的分层控制框架，并在真实海况数据驱动的MuJoCo实仿设置中验证。

核心要点

• 高海况中USV运动剧烈且快速变化，短历史隐式推断限制了现有分层降落方法的适应性。
• 框架在高层降落决策中引入预训练模型，显式预测USV姿态以识别合适着陆窗口。
• 预测结果用于选择触地时机，低层常规飞控负责保持UAV稳定并跟踪运动指令。
• 实验表明显式姿态预测可改进着陆窗口选择，并支持面向时机的UAV降落规划。

验证范围与局限：验证限于真实海况USV运动数据驱动的MuJoCo实仿设置。

英文摘要 (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.

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

来源

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1. arXiv 2610.13190：Prediction-Informed Landing Window Planning for UAV Landing on USV in High Sea States访问 2026-10-21