Efficient trajectory generation in complex dynamic environment stills remains an open problem in the unmanned surface vehicle (USV) domain. In this paper, a cooperative trajectory planning algorithm for the coupled USV-UAV system is proposed, to ensure that USV can execute safe and smooth path in the process of autonomous advance in multi obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role as a flight sensor, and it provides real-time global map and obstacle information with 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 whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.
翻译:复杂的动态环境中的高效轨迹生成仍然是无人驾驶地面飞行器(USV)域的一个未解决的问题。本文提议为配有USV-UAV系统的配合轨迹规划算法,以确保USV能够在多障碍图的自主推进过程中执行安全和平稳的路径。具体地说,无人驾驶飞行器(UAV)起着飞行传感器的作用,它通过轻量分解网络和三维投影转换提供实时全球地图和障碍信息。然后,以图表为基础的搜索方法生成了最初的避免障碍轨迹。关于USV独特的活性动力特性,采用了基于船体动态限制的数字优化方法,以便于跟踪运动控制轨道。最后,提出了以NMPC为基础的运动控制方法,其执行过程中的能量消耗限制最小。实验结果验证了整个系统的有效性,产生的轨迹对USV具有相当准确性的局部最佳跟踪。