﻿ 海上交通高峰航线船舶疏导路径规划仿真分析
 舰船科学技术  2023, Vol. 45 Issue (22): 206-209    DOI: 10.3404/j.issn.1672-7649.2023.22.040 PDF

Simulation analysis of ship guidance path planning on maritime traffic peak routes
GUO Xing-hua, ZHAO Cang-long
Technology College of Shipping, Jiangsu Shipping College, Nantong 226010, China
Abstract: In the face of the complexity of ship evacuation path planning during peak sea traffic routes, a simulation analysis method for ship evacuation path planning during peak sea traffic routes is studied to determine the optimal ship evacuation path. Build an objective function for ship evacuation path planning on peak maritime traffic routes with the minimum range, safety, and smoothness. Use grid method to simulate the ship environment on peak maritime traffic routes, implement modeling using simulation software, and improve genetic algorithm to find the global optimal solution of the ship evacuation path planning model on peak maritime traffic routes. Using non linear programming to solve the local optimal solution of the ship evacuation path planning model for maritime traffic peak routes, determine the optimal ship evacuation path, and achieve simulation of ship evacuation path planning for maritime traffic peak routes. The simulation results show that this method has good directionality for ship evacuation paths on peak sea traffic routes in two scenarios, and the planned optimal evacuation path has the smallest safety, smoothness, and range, which conforms to the objective function of ship evacuation paths on peak sea traffic routes.
Key words: maritime traffic     peak route     ship grooming     path planning     simulation analysis     hybrid genetic algorithm
0 引　言

1 海上交通高峰航线船舶疏导路径规划仿真 1.1 海上交通高峰航线船舶疏导路径规划问题描述

 $\left\{\begin{gathered}\min f_1\left(Q\right)=length\left(Q\right)\text{，} \\ \min f_2\left(Q\right)=safety\left(Q\right)\text{，} \\ \min f_3\left(Q\right)=smoothness\left(Q\right)\text{。} \\ \end{gathered}\right.$ (1)

 $Q = \left[ {A = {k_0},{k_1},{k_2}, \cdots ,{k_n},{k_{n + 1}} = B} \right] \text{。}$ (2)

 $length\left( Q \right) = \sum\limits_{i = 1}^n {dis\left( {{k_i},{k_{i + 1}}} \right)}\text{，}$ (3)
 $dis\left( {{k_i},{k_{i + 1}}} \right) = \sqrt {{{\left( {{x_i} - {x_{i + 1}}} \right)}^2} + {{\left( {{y_i} - {y_{i + 1}}} \right)}^2}} \text{。}$ (4)

 $safety\left( Q \right) = - \mathop {\min }\limits_{0 \leqslant i \leqslant n} \mathop {\min }\limits_{0 \leqslant j \leqslant m} \left\{ {MinDis\left( {\overline {{k_i}{k_{i + I}}} ,{G_j}} \right)} \right\}\text{。}$ (5)

 $smoothness\left( Q \right) = \left( {1/n} \right)\sum\limits_{i = 0}^{n - 1} {\left( {\varepsilon \left[ {{k_i},{k_{i + 1}},{k_{i + 1}}} \right]} \right)} \text{。}$ (6)
 $\begin{split} & \varepsilon[k_i,k_{i+1},k_{i+1}]={\text π}-\cos^{-1} \\ & \left[\frac{(y_{i+1}-y_i)(y_{i+2}-y_{i+1})}{dis\left(k_i,k_{i+1}\right)dis(k_{i+1},k_{i+2})}+(x_{i+1}-x_i)(x_{i+2}-x_{i-1})\right]\text{。}\end{split}$ (7)

1.2 基于混合遗传算法的海上交通高峰航线船舶疏导路径规划方法

 $Map\left(\alpha \right)=\;\Biggr\{ \begin{array}{l}D,栅格能通过\text{，}\\ 0,栅格不能通过\text{。}\end{array}$ (8)

 $f = \left\{ \begin{gathered} \chi \left| {{H_c}} \right| + \eta {H_l}{,^{}}{H_l} < {H_s}\text{，} \\ \chi \left| {{H_c}} \right| + \mu {\left( {{H_l} - {H_s}}\right)^{}},{H_l} > {H_s}\text{。} \\ \end{gathered} \right.$ (9)

① 通过轮盘赌法实施遗传操作选取生命力强的染色体即疏导路径。

② 通过算数交叉操作得出新的基因组，即新的疏导路径组合。

③ 利用变异算子得出均值和方差替换以前基因值，即变异后得出新疏导路径。

④ 安全算子。海上交通高峰航线船舶疏导路径需要规避障碍物，在栅格中选取离障碍物最优栅格并将该栅格节点设置在船舶疏导路径中。

⑤ 平滑算子。利用平滑算子求解船舶疏导路径最大转向角，保证船舶疏导过程的稳定性。

⑥ 最短路径算子。通过最短路径算子寻找海上交通高峰航线船舶疏导路径的最短航程。

2 结果实验分析

 图 1 海上交通高峰航线船舶疏导路径 Fig. 1 Ship diversion paths on maritime traffic peak routes

 图 2 复杂场景下本文方法的疏导路径仿真图 Fig. 2 Simulation of the grooming path of our method in complex scenarios
3 结　语

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