Recovering a drone on a disturbed water surface remains a significant challenge in maritime robotics. In this paper, we propose a unified framework for robot-assisted drone recovery on a wavy surface that addresses two major tasks: Firstly, accurate prediction of a moving drone's position under wave-induced disturbances using KalmanNet Plus Plus (KalmanNet++), a Neural Network Aided Kalman Filtering we proposed. Secondly, effective motion planning using the desired position we got for a manipulator via Receding Horizon Model Predictive Control (RHMPC). Specifically, we compared multiple prediction methods and proposed KalmanNet Plus Plus to predict the position of the UAV, thereby obtaining the desired position. The KalmanNet++ predicts the drone's future position 0.1\,s ahead, while the manipulator plans a capture trajectory in real time, thus overcoming not only wave-induced base motions but also limited constraints such as torque constraints and joint constraints. For the system design, we provide a collaborative system, comprising a manipulator subsystem and a UAV subsystem, enables drone lifting and drone recovery. Simulation and real-world experiments using wave-disturbed motion data demonstrate that our approach achieves a high success rate - above 95\% and outperforms conventional baseline methods by up to 10\% in efficiency and 20\% in precision. The results underscore the feasibility and robustness of our system, which achieves state-of-the-art performance and offers a practical solution for maritime drone operations.

翻译：在受扰动的海面上回收无人机仍然是海事机器人领域的一项重大挑战。本文提出了一种用于波浪表面机器人辅助无人机回收的统一框架，该框架解决了两个主要任务：首先，使用我们提出的神经网络辅助卡尔曼滤波方法——KalmanNet++，精确预测波浪扰动下移动无人机的位置；其次，利用通过滚动时域模型预测控制（RHMPC）获得的期望位置，为机械臂进行有效的运动规划。具体而言，我们比较了多种预测方法，并提出KalmanNet++来预测无人机的位置，从而获得期望位置。KalmanNet++能够提前0.1秒预测无人机的未来位置，而机械臂则实时规划捕获轨迹，从而不仅克服波浪引起的基础运动，还解决了扭矩约束和关节约束等有限约束问题。在系统设计方面，我们提供了一个由机械臂子系统和无人机子系统组成的协作系统，实现了无人机的提升与回收。利用波浪扰动运动数据进行的仿真和真实世界实验表明，我们的方法实现了高达95%以上的成功率，在效率上比传统基线方法提升高达10%，在精度上提升高达20%。这些结果凸显了我们系统的可行性和鲁棒性，该系统达到了最先进的性能水平，并为海事无人机作业提供了实用的解决方案。