College of Mathematics and Computer, Guangdong Ocean University, Zhanjiang 524091, China.
Maritime College, Guangdong Ocean University, Zhanjiang 524091, China.
Sensors (Basel). 2023 May 8;23(9):4567. doi: 10.3390/s23094567.
Ship collision avoidance is a complex process that is influenced by numerous factors. In this study, we propose a novel method called the Optimal Collision Avoidance Point (OCAP) for unmanned surface vehicles (USVs) to determine when to take appropriate actions to avoid collisions. The approach combines a model that accounts for the two degrees of freedom in USV dynamics with a velocity obstacle method for obstacle detection and avoidance. The method calculates the change in the USV's navigation state based on the critical condition of collision avoidance. First, the coordinates of the optimal collision avoidance point in the current ship encounter state are calculated based on the relative velocities and kinematic parameters of the USV and obstacles. Then, the increments of the vessel's linear velocity and heading angle that can reach the optimal collision avoidance point are set as a constraint for dynamic window sampling. Finally, the algorithm evaluates the probabilities of collision hazards for trajectories that satisfy the critical condition and uses the resulting collision avoidance probability value as a criterion for course assessment. The resulting collision avoidance algorithm is optimized for USV maneuverability and is capable of handling multiple moving obstacles in real-time. Experimental results show that the OCAP algorithm has higher and more robust path-finding efficiency than the other two algorithms when the dynamic obstacle density is higher.
船舶避碰是一个复杂的过程，受到许多因素的影响。在本研究中，我们提出了一种新的方法，称为无人水面舰艇（USV）的最佳避碰点（OCAP），以确定何时采取适当的行动来避免碰撞。该方法结合了一个考虑 USV 动力学两个自由度的模型和一个用于障碍物检测和避碰的速度障碍方法。该方法根据避碰的临界条件计算 USV 导航状态的变化。首先，根据 USV 和障碍物的相对速度和运动学参数，计算当前船舶相遇状态下的最佳避碰点的坐标。然后，将到达最佳避碰点的船舶线速度和航向角的增量设置为动态窗口采样的约束条件。最后，算法评估满足临界条件的轨迹的碰撞危险概率，并使用得到的避碰概率值作为航线评估的标准。所得到的避碰算法针对 USV 的机动性进行了优化，能够实时处理多个移动障碍物。实验结果表明，在动态障碍物密度较高时，OCAP 算法的路径寻优效率比其他两种算法更高、更稳健。
