为优化欠驱动船舶自动靠泊的轨迹规划问题，避免陷入局部最优，提出一种考虑船舶运动学模型整体约束和障碍物约束的实时轨迹规划方法。在栅格化地图基础上，基于A*算法规划全局最优路径，运用冗余点删除策略得到轨迹规划的参考航路点。考虑到船舶靠泊为动态过程，基于扩展动态窗口算法(EDWA)对船舶恒力阶段和减速阶段的靠泊轨迹进行预测，并引入比例积分微分(PID)控制方法调整船舶航向。对所有预测轨迹进行可行性检验后，利用考虑目标点、参考航路点和障碍物等约束的评价函数得到最优轨迹，其相对应的力对作为船舶的控制输入。在下一个采样时间基于船舶当前的控制输入和运动状态重复以上步骤进行实时规划，直到船舶到达目标点。以日照港为仿真场景验证本文方法的可行性，结果表明，该方法能够在较短时间内引导船舶避开障碍物到达指定泊位，轨迹与最近障碍物的距离为144.1428 m，轨迹长度较传统A*+EDWA长47.26 m，但规划时间减少了55.12%，表明了本文方法的优越性。

To optimize the trajectory planning problem of underactuated ship automatic berthing and avoid getting stuck in local optima, a real-time trajectory planning method considering the overall constraints of the ship kinematic model and obstacle constraints was proposed. Based on the gridded map, the A* algorithm was used to plan the global optimal path, and the redundant point deletion strategy was applied to obtain the reference waypoints for trajectory planning. Considering that ship berthing was a dynamic process, the extended dynamic window algorithm (EDWA) was used to predict the berthing trajectory of the ship during the constant force stage and deceleration stage, and the proportional integral derivative (PID) control method was introduced to adjust the ship’s heading. After conducting feasibility tests on all predicted trajectories, the optimal trajectory was obtained by using an evaluation function that considers constraints such as target points, reference waypoints, and obstacles, and the corresponding force was used as the control input for the ship. Repeat the above steps for realtime planning based on the current control input and motion state of the ship at the next sampling time until the ship reached the target point. The feasibility of the proposed method was verified by using the simulation scenario of Rizhao port. Results show that the proposed method can guide ships to avoid obstacles and reach the designated berth in a relatively short time, the distance between the trajectory and the nearest obstacle is 144.1428 m, and the trajectory length is 47.26 m longer than the traditional A*+EDWA, however, the planning time is reduced by 55.12%, which show the superiority of the proposed method.