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:在复杂的动态环境中高效轨道生成仍然是无人驾驶地面飞行器(USV)的一个未决问题。对USV的印象通常受到船体摆动和环境天气的干扰,因此很难规划最佳的USV轨迹。在本文中,提议为配合的USV-UAV系统制定合作轨迹规划算法,以确保USV能够在多孔地图的自主推进过程中安全、顺利地执行一条路径。具体地说,无人驾驶航空器(UAV)发挥飞行传感器的作用,提供实时全球地图和障碍信息,使用轻量语分解网络和3D投影转换。然后,最初的避免障碍轨迹由基于图表的搜索方法产生。关于USV独特的低活性运动特征,采用了基于机体动态限制的数值优化方法,以便更容易跟踪运动控制轨迹。最后,提出了以NMPC为基础的运动控制方法,其执行期间的能量消耗限制最小。实验结果验证了整个系统的有效性,而生成的轨迹轨迹则对USV具有相当的精确性。