Efficient trajectory generation in complex dynamic environments remains an open problem in the unmanned surface vehicle (USV). The perception of the USV is usually interfered with by the swing of the hull and the ambient weather, making it challenging to plan the optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for the coupled USV-UAV system is proposed to ensure that USV can execute a safe and smooth path in the process of autonomous advance in multi-obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role of a flight sensor, providing real-time global map and obstacle information with a lightweight semantic segmentation network and 3D projection transformation. And then, an initial obstacle avoidance trajectory is generated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the effectiveness of the whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.

翻译：在复杂动态环境中高效生成轨迹仍是无人水面艇（USV）领域的一个开放性问题。USV的感知通常受船体摇晃和周围天气干扰，难以规划最优轨迹。针对这一问题，本文提出了一种面向USV-UAV耦合系统的协同轨迹规划算法，以确保无人水面艇在多障碍物地图中自主前进时能够执行安全且平滑的路径。具体而言，无人机（UAV）充当飞行传感器角色，通过轻量级语义分割网络和三维投影变换提供实时全局地图和障碍物信息。随后，基于图搜索方法生成初始避障轨迹。考虑到USV独特的欠驱动运动学特性，引入一种基于船体动力学约束的数值优化方法，使得轨迹更易于被运动控制跟踪。最后，提出了一种基于NMPC的运动控制方法，并在执行过程中施加最低能耗约束。实验结果验证了该系统的有效性，生成的轨迹对USV而言是局部最优的，且具有较高的跟踪精度。