基于多模态智能体AI和主动波浪补偿的自主无人机稳健海上平台着陆
Robust Autonomous UAV Landing on Maritime Platforms via Multimodal Agentic AI and Active Wave Compensation
AI总结：
提出一种解耦的多飞行器着陆框架，利用双深度强化学习智能体（SAC和模态RL）实现无人机在海上平台的稳健着陆，在15次试验中达到100%成功率。
摘要：
海洋基础设施的自主空中检查经常受到随机海况的影响，带来高动能冲击、着陆后倾覆和传感器遮挡的风险。本文提出一种解耦的多飞行器着陆框架，将配备3-RPU稳定平台的无人水面艇（USV）与稳健的无人机（UAV）同步。该架构利用两个独立的深度强化学习（DRL）智能体：一个软演员-评论家（SAC）智能体为着陆甲板提供高频波浪运动补偿，另一个多模态RL智能体用于无人机的最终进场。在高保真海洋模拟中评估，该系统在从平静到汹涌的波浪状态下进行的15次试验中实现了100%的着陆成功率。结果显示，平均稳定效率为87.8%，在汹涌条件下，96%的任务时间内着陆表面保持在水平面1度以内，有效促进了更安全的着陆。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
