  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.
