无人机海上平台精准着陆，靠多模态AI与主动波浪补偿
Robust Autonomous UAV Landing on Maritime Platforms via Multimodal Agentic AI and Active Wave Compensation
- 分步控制：水面艇稳平台+无人机自主降落双强化学习
- 15次测试全成功，恶劣海况下96%时间保持水平误差<1度
- 适合海上巡检、无人舰载系统研发人员参考
海上设施自主空中巡检常受随机海况影响，易引发高动能撞击、着陆后倾覆及传感器遮挡。本文提出一种解耦的多机着陆框架，通过配备3-RPU稳定平台的无人水面艇（USV）协同具备鲁棒性的无人机（UAV）。系统采用两个独立的深度强化学习（DRL）智能体：软演员-评论家（SAC）智能体实现高频波浪运动补偿，多模态强化学习智能体负责无人机最后着陆阶段。在高保真海洋仿真中评估，系统在从平静到剧烈海况下共15次试验中达成100%着陆成功率。结果显示平均稳定效能达87.8%，在恶劣条件下96%任务时间内将着陆面维持在水平面±1度以内，显著提升着陆安全性。
原文摘要 · Abstract (English)
Autonomous aerial inspection of marine infrastructure is frequently compromised by stochastic sea states, introducing risks of high-kinetic impacts, post-landing toppling, and sensory occlusion. This paper proposes a decoupled, multi-vehicle landing framework synchronizing an Unmanned Surface Vehicle (USV) equipped with a 3-RPU stabilized platform with a robust Unmanned Aerial Vehicle (UAV). The architecture utilizes two independent Deep Reinforcement Learning (DRL) agents: a Soft Actor-Critic (SAC) agent providing high-frequency wave-motion compensation for the landing deck, and a multimodal RL agent for the UAVs final approach. Evaluated in high-fidelity maritime simulations, the system achieved a 100% landing success rate across 15 trials in wave states varying from calm to rough. Results show a mean stabilization efficacy of 87.8%, maintaining the landing surface within 1 degree of the horizontal plane for 96% of the mission duration in rough conditions, effectively contributing to safer landings.
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