针对现有算法求解大规模船舶路径规划问题性能不佳且对涡旋等海洋环境因素考虑较少等问题，提出了一种基于惩罚信息素蚁群优化算法（punishment pheromone ant colony optimization, PPACO）的船舶路径规划方法。首先，设计了长度、风险和航向3个评价函数；其次利用强化学习启发的蚁群优化算法来搜索最优路径，在传统引导信息素的基础上加入了惩罚信息素，可以防止蚂蚁的无效搜索；最后对改进算法进行仿真实验，在静态环境下的规划结果表明，在路径长度、风险值和航向累积角等指标方面，本文算法相比较传统蚁群算法（ant colony optimization, ACO）、跳点搜索算法（jump point search, JPS）和双向搜索的改进蚁群算法表现出更优越的性能，相对于3个对比算法的最好指标，路径长度减少6.1%，风险值降低5.6%，航向积累角减少78.6%，迭代次数减少53.3%，改进显著。特别是在引入了中尺度涡旋和水流运动因素的情况下，本文算法仍能规划出更优的船舶航行路径，有着积极的应用意义。

Aiming at the poor performance of existing algorithms in solving large-scale ship path planning problems and the lack of consideration of marine environmental factors such as eddies, a ship path planning method based on punishment pheromone ant colony optimization was proposed. Firstly, three evaluation functions were designed for the planned path: length, risk and heading. Secondly, ACO(ant colony optimization) algorithm inspired by reinforcement learning was designed to search the optimal path, which adds punishment pheromone to the traditional guidance pheromone, which can prevent ants from conducting ineffective searches. Finally, the simulation experiments of the improved algorithm under static environments demonstrate that the proposed algorithm is superior to traditional ACO, jump point search algorithm, and bi-directional search improved ACO in terms of path length, risk value and turn accumulation angle. Compared to the best metrics among these three algorithms, proposed algorithm still achieves a significant improvement in path length reduction of 6.1%, risk value reduction of 5.6%, heading accumulation angle reduction of 78.6%, and iteration number reduction of 53.3%. Especially when the mesoscale eddies and water flow are introduced, the proposed algorithm can still plan a more suitable path for ship navigation, which has positive application significance.