In this paper we propose a novel distributed model predictive control (DMPC) based algorithm with a trajectory predictor for a scenario of landing of unmanned aerial vehicles (UAVs) on a moving unmanned surface vehicle (USV). The algorithm is executing DMPC with exchange of trajectories between the agents at a sufficient rate. In the case of loss of communication, and given the sensor setup, agents are predicting the trajectories of other agents based on the available measurements and prior information. The predictions are then used as the reference inputs to DMPC. During the landing, the followers are tasked with avoidance of USV-dependent obstacles and inter-agent collisions. In the proposed distributed algorithm, all agents solve their local optimization problem in parallel and we prove the convergence of the proposed algorithm. Finally, the simulation results support the theoretical findings.

翻译：本文提出了一种新颖的基于分布式模型预测控制（DMPC）的算法，并结合轨迹预测器，用于实现无人机（UAV）在移动无人水面舰艇（USV）上着陆的场景。该算法以足够快的频率执行DMPC并在各智能体之间交换轨迹。在通信丢失且基于传感器配置的情况下，各智能体根据可用的测量值和先验信息预测其他智能体的轨迹。这些预测随后被用作DMPC的参考输入。在着陆过程中，跟随者的任务是避开依赖于USV的障碍物以及避免智能体间的碰撞。在所提出的分布式算法中，所有智能体并行求解其局部优化问题，我们证明了该算法的收敛性。最后，仿真结果支持了理论发现。