基于模型预测控制的无人机在移动船只上敏捷着陆的轨迹生成
Model predictive control-based trajectory generation for agile landing of unmanned aerial vehicle on a moving boat
AI总结：
本研究提出一种基于MPC的新型轨迹生成方法，利用USV状态预测为多旋翼UAV生成周期更新轨迹，通过动态调整惩罚矩阵及姿态同步，实现恶劣海况下移动倾斜甲板的快速精准着陆，精度达现有方法两倍且机载实时计算。
摘要：
本文提出了一种基于模型预测控制（Model Predictive Control, MPC）的新型轨迹生成方法，用于在恶劣条件下将无人机（Unmanned Aerial Vehicle, UAV）敏捷着陆到无人水面艇（Unmanned Surface Vehicle, USV）的甲板上。该轨迹生成方法利用USV的状态预测，为多旋翼UAV生成周期性更新的轨迹，即使在甲板倾角持续变化的情况下，也能精准着陆在移动的USV甲板上。我们采用基于MPC的方案生成轨迹，同时考虑UAV动力学以及USV的预测状态（最高到位置和姿态的一阶导数）。与现有方法相比，我们的方法会根据飞行阶段动态调整惩罚矩阵，以精准跟踪对应状态。尤其在着陆机动阶段，UAV会与USV的姿态同步，从而在倾斜甲板上实现快速着陆。仿真结果表明，该方法在包括大浪（浪高4米）在内的多种海况下均具有可靠性，在着陆速度和精度上优于现有最优方法，平均精度提升一倍。最后，真实世界实验验证了仿真结果，证明了该方法可在移动USV上实现鲁棒着陆，且所有计算均在UAV机载实时完成。
英文摘要：
This paper proposes a novel trajectory generation method based on Model Predictive Control (MPC) for agile landing of an Unmanned Aerial Vehicle (UAV) onto an Unmanned Surface Vehicle (USV)'s deck in harsh conditions. The trajectory generation exploits the state predictions of the USV to create periodically updated trajectories for a multirotor UAV to precisely land on the deck of a moving USV even in cases where the deck's inclination is continuously changing. We use an MPC-based scheme to create trajectories that consider both the UAV dynamics and the predicted states of the USV up to the first derivative of position and orientation. Compared to existing approaches, our method dynamically modifies the penalization matrices to precisely follow the corresponding states with respect to the flight phase. Especially during the landing maneuver, the UAV synchronizes attitude with the USV's, allowing for fast landing on a tilted deck. Simulations show the method's reliability in various sea conditions up to Rough sea (wave height 4 m), outperforming state-of-the-art methods in landing speed and accuracy, with twice the precision on average. Finally, real-world experiments validate the simulation results, demonstrating robust landings on a moving USV, while all computations are performed in real-time onboard the UAV.
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。
