利用无人机对受波浪影响的海洋船舶进行状态估计
State estimation of marine vessels affected by waves by unmanned aerial vehicles
AI总结：
针对波浪扰动下的USV，提出融合UAV与USV多传感器数据的6自由度非线性状态估计与预测方法，并经仿真和真实降落实验验证其优于现有方法。
摘要：
本文提出了一种在汹涌海况下对海洋船舶进行鲁棒状态估计的新方法，旨在不受天气条件影响，实现无人机（Unmanned Aerial Vehicles，UAV）与海洋船舶之间的紧密协作，例如协同降落或物体操作。我们对受强烈波浪运动影响的海洋船舶（本文中为无人水面艇，Unmanned Surface Vehicle，USV）动力学进行了研究，由此提出了一种具有6个自由度（DOF）的新型非线性USV数学模型，这是精确进行USV状态估计和运动预测所必需的。所提出的状态估计与预测方法融合了UAV和USV搭载的多种传感器数据，以在实际应用中不断变化的天气条件下实现冗余性和鲁棒性。该方法可提供USV的6个DOF估计状态，并预测其未来状态，从而在滚动控制时域内实现对两个运载器的紧密控制。所提方法已在逼真的Gazebo仿真器中进行了广泛测试，并在代表不同应用场景的多项真实世界实验中成功得到实验验证，包括在摇摆且移动的USV上进行敏捷降落。对比研究表明，所提方法显著优于当前最先进方法。
英文摘要：
A novel approach for robust state estimation of marine vessels in rough water is proposed in this paper to enable tight collaboration between Unmanned Aerial Vehicles (UAVs) and a marine vessel, such as cooperative landing or object manipulation, regardless of weather conditions. Our study of marine vessel (in our case Unmanned Surface Vehicle (USV)) dynamics influenced by strong wave motion has resulted in a novel nonlinear mathematical USV model with 6 degrees of freedom (DOFs), which is required for precise USV state estimation and motion prediction. The proposed state estimation and prediction approach fuses data from multiple sensors onboard the UAV and the USV to enable redundancy and robustness under varying weather conditions of real-world applications. The proposed approach provides estimated states of the USV with 6 DOFs and predicts its future states to enable tight control of both vehicles on a receding control horizon. The proposed approach was extensively tested in the realistic Gazebo simulator and successfully experimentally validated in many real-world experiments representing different application scenarios, including agile landing on an oscillating and moving USV. A comparative study indicates that the proposed approach significantly surpassed the current state-of-the-art.
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