Unmanned surface vessels (USVs) are widely used in ocean exploration and environmental protection fields. To ensure that USV can successfully perform its mission, trajectory planning and motion tracking are the two most critical technologies. In this paper, we propose a novel trajectory generation and tracking method for USV based on optimization theory. Specifically, the USV dynamic model is described with differential flatness, so that the trajectory can be generated by dynamic RRT* in a linear invariant system expression form under the objective of optimal boundary value. To reduce the sample number and improve efficiency, we adjust the trajectory through local optimization. The dynamic constraints are considered in the optimization process so that the generated trajectory conforms to the kinematic characteristics of the under-actuated hull, and makes it easier to be tracked. Finally, motion tracking is added with model predictive control under a sequential quadratic programming problem. Experimental results show the planned trajectory is more in line with the kinematic characteristics of USV, and the tracking accuracy remains a higher level.
翻译:无人驾驶的地表船只(USVs)被广泛用于海洋勘探和环境保护领域。为了确保USV能够成功完成任务,轨迹规划和运动跟踪是两项最重要的技术。在本文中,我们提出了基于优化理论的USV新型轨迹生成和跟踪方法。具体地说,USV动态模型被描述为不同平坦,这样,轨迹可以在最佳边界值目标下以线性变数系统表达形式生成。为了减少样本数量并提高效率,我们通过本地优化调整轨迹。在优化过程中考虑了动态限制,以使生成的轨迹符合下活化船体的动态特征,并更容易跟踪。最后,在连续四极编程问题下,运动跟踪与模型预测控制相加。实验结果显示,计划轨迹与USV的动性特征更加一致,跟踪准确度仍然更高。