用无人机精准估算波动海面中无人船状态，支持复杂协同操作。
State estimation of marine vessels affected by waves by unmanned aerial vehicles
- 构建6自由度非线性模型，捕捉强浪下无人船运动特性。
- 融合机载与船载传感器数据，实现恶劣天气下稳定状态估计。
- 支持动态着陆等高精度协同任务，实测表现超越现有方法。
本文提出一种新型鲁棒状态估计算法，用于在恶劣海况下对海洋船只（本研究中为无人水面艇，USV）进行精确状态估计，以实现无人机（UAV）与无人船之间的紧密协同，如协同着陆或物体操控。针对强浪作用下的无人船动力学特性，构建了具有6个自由度（DOFs）的新型非线性数学模型，为精确的状态估计与运动预测提供基础。所提方法融合无人机与无人船上的多源传感器数据，实现冗余与鲁棒性，在真实应用的多变天气条件下表现优异。该方法可输出无人船6自由度状态并预测其未来状态，支持在递推控制时域内对两车进行精确控制。算法在真实的Gazebo仿真环境中广泛测试，并在多种实际场景实验中成功验证，包括在振荡移动的无人船上实现敏捷着陆。对比研究表明，该方法显著优于当前最先进水平。
原文摘要 · Abstract (English)
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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