以保障船舶安全航行为基础，以提升船舶续航能力为目的，研究复杂海面环境下船舶航行最优路径规划数学模型。利用概率路图法将船舶航行的、具有复杂海面环境特征的全局海域环境转换成离散空间，采用连接采样点获取离散空间若干条由初始点至目的点的路径，生成路线图。依照某船舶航行性能评价指标，构建航行路线图中的最优路径规划数学模型，考虑复杂海面环境设定地形与威胁约束、船舶转弯角度约束以及路径平滑度约束等。采用基于震荡型入侵野草优化算法求解数学模型，在所构建的路线图内获取一个符合标准的解向量，即最优路径规划结果。实验结果显示，该模型能够有效获取最优路径规划结果，以能耗最低为船舶航行性能评价指标时更利于船舶续航。
In order to ensure the safe navigation of ships and improve the endurance of ships, the mathematical model of optimal path planning of ships in complex sea environment is studied. The probability road map method is used to convert the global sea environment with complex sea surface environment characteristics of the ship navigation into discrete space, and several paths from the initial point to the destination point in the discrete space are obtained by connecting the sampling points to generate the road map. According to the navigation performance evaluation index of a ship, the mathematical model of optimal path planning in the navigation route map of a ship is constructed. The terrain and threat constraints, ship turning angle constraints, and path smoothness constraints set in the complex sea environment are considered. The mathematical model is solved by using the optimization algorithm based on concussion invasive weeds, and a standard solution vector is obtained in the constructed road map, that is, the optimal path planning result. The experimental results show that the model can effectively obtain the optimal path planning results, and it is more conducive to ship endurance when taking the lowest energy consumption as the evaluation index of ship navigation performance.
2022,44(21): 59-62 收稿日期：2022-06-21
DOI：10.3404/j.issn.1672-7649.2022.21.013
分类号：TP301.6
基金项目：吉林省高等教育教学改革研究课题立项支持项目（JLJY202128513006）
作者简介：王俊彦(1980-),女,硕士,副教授,研究方向为数学教育与应用数学
参考文献：
[1] 童帮裕, 胡坚堃. 基于改进蚁群算法的船舶冰区航行路径规划[J]. 中国航海, 2020, 43(1): 24–28
TONG Bangyu, HU Jiankun. Improved ant colony optimization for navigation path planning in ice zone[J]. Navigation of China, 2020, 43(1): 24–28
[2] 蒋仁炎, 俞万能, 廖卫强, 等. 智能全电船的低能耗路径规划算法研究[J]. 中国造船, 2021, 62(2): 245–254
JIANG Renyan, YU Wanneng, LIAO Weiqiang, et al. Optimal energy consumption based path planning for intelligent all-electric ships[J]. Shipbuilding of China, 2021, 62(2): 245–254
[3] 姚绪梁, 王峰, 王景芳, 等. 不确定海流环境下水下机器人最优时间路径规划[J]. 控制理论与应用, 2020, 37(6): 1302–1310
YAO Xuliang, WANG Feng, WANG Jingfang, et al. Time-optimal path planning for autonomous underwater vehicles with uncertain ocean[J]. Control Theory & Applications, 2020, 37(6): 1302–1310
[4] 刘雨青, 向军, 曹守启. 基于改进蚁群算法的水下自主航行机器人路径规划[J]. 计算机工程与科学, 2022, 44(3): 536–544
LIU Yuqing, XIANG Jun, CAO Shouqi. AUV path planning based on improved ant colony algorithm[J]. Computer Engineering and Science, 2022, 44(3): 536–544
[5] 黄鹤, 李潇磊, 杨澜, 等. 引入改进蝠鲼觅食优化算法的水下无人航行器三维路径规划[J]. 西安交通大学学报, 2022, 56(7): 9–18
HUANG He, LI Xiaolei, YANG Lan, et al. Three dimensional path planning of unmanned underwater vehicle based on improved[J]. Journal of Xi'an Jiaotong University, 2022, 56(7): 9–18
[6] 王鑫, 田艺, 蒋华, 等. 基于锚节点移动路径动态规划的定位算法[J]. 计算机工程与设计, 2020, 41(8): 2135–2140
WANG Xin, TIAN Yi, JIANG Hua, et al. Location algorithm based on dynamic planning of anchor node moving path[J]. Computer Engineering and Design, 2020, 41(8): 2135–2140
[7] 赵苗, 高永琪, 吴笛霄, 等. 复杂海战场环境下AUV全局路径规划方法[J]. 国防科技大学学报, 2021, 43(1): 41–48
ZHAO Miao, GAO Yongqi, WU Dixiao, et al. AUV global path planning method in complex sea battle field environment[J]. Journal of National University of Defense Technology, 2021, 43(1): 41–48
