﻿ 基于鱼群算法的物流运输船舶港口避撞路径跟踪控制
 舰船科学技术  2023, Vol. 45 Issue (24): 208-211    DOI: 10.3404/j.issn.1672-7649.2023.24.040 PDF

1. 南京航空航天大学，江苏 南京 210016;
2. 江苏海事职业技术学院，江苏 南京 211170

Port collision avoidance path tracking control of logistics transport ships based on fish swarm algorithm
WANG Peng1,2
1. Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2. Jiangsu Maritime Institute, Nanjing 211170, China
Abstract: In order to improve the safety of logistics transportation and realize more intelligent port operation and management, the port collision avoidance path tracking control of logistics transport ships based on fish swarm algorithm is proposed. Taking the shortest collision avoidance path as the objective function of the collision avoidance path tracking control, the weighted average visual range and adaptive crowding factor are introduced into the fish swarm algorithm, and the shortest collision avoidance path of the port of the logistics transport ship is found through the foraging, clustering and rear-end behaviors of the fish swarm, as the final collision avoidance path tracking control result. The experimental results show that the proposed method can dynamically adjust the path according to the real-time obstacle information, and provide accurate and reliable data support for ship port collision avoidance path tracking control.
Key words: logistics transportation     fish swarm algorithm     collision avoidance path     tracking control     visual range     crowding factor
0 引　言

1 物流运输船舶港口避撞路径跟踪控制 1.1 船舶会遇情形分析

1.2 避撞路径跟踪控制目标函数

 ${d_s} = {T_a}{V_o}{\text{，}}$ (2)
 ${d_r} = {T_a}{V_o}\sin C_o'/\left| {\sin C_b'} \right|{\text{。}}$ (3)

 $C_{OT}' = C_o' - {C_T} {\text{，}}$ (7)
 $V_R' = \sqrt {{V^2} + V_o^2 - 2{V_T}{V_o}\cos C_{OT}'}{\text{。}}$ (8)

1.3 栅格建模法

 图 1 船舶运输栅格模型 Fig. 1 Grid model of ship transportation
1.4 改进鱼群算法的目标函数求解

 $h = \sqrt {{{\left( {{x_f} - {x_g}} \right)}^2} + {{\left( {{y_f} - {y_g}} \right)}^2}} {\text{，}}$ (9)

$F\left( {P\left( {{x_f},{y_f}} \right)} \right) = \left\{ {g\left| {g \in A,d\left( {g,P\left( {{x_f},{y_f}} \right)} \right) \leqslant r} \right.} \right\}$，表示鱼群视野范围，$r$为视野范围半径；在栅格法模型内，鱼群视野范围为一个$r \times r$的正方形范围。

 ${g_c} = F\left( {P\left( {{x_f},{y_f}} \right)} \right)\left( {\sum\limits_{j = 1}^{{N_f}} {{g_j}} } \right)/{N_f}{\text{。}}$ (10)

 ${g}_{n}=\left\{\begin{array}{l} {g}_{c},{h}_{gc}\cdot {N}_{f}\leqslant I\cdot {h}_{{g}_{i}}{\text{，}}{g}_{c}\in {g}_{i}{\text{，}} \\ {\mathrm{arg}}\;\mathrm{min}\left\{d\left(g,{g}_{c}\right)\right\},{h}_{gc}\cdot {N}_{f}\leqslant I\cdot {h}_{{g}_{i}}{\text{，}}{g\in {g}_{i}}{\text{，}} \\ -\infty ,{h}_{gc}\cdot {N}_{f} > I\cdot {h}_{gi}{\text{。}} \end{array} \right.$ (11)

 ${g}_{n}=\left\{\begin{array}{l}{g}_{j},{h}_{gi}\cdot{N}_{f}\leqslant I\cdot{h}_{gi}{\text{，}}{g}_{j}\in {g}_{i}{\text{，}} \\ {\mathrm{arg}}\;\mathrm{min}\left\{d\left(g,{g}_{j}\right)\right\},{h}_{gi}\cdot{N}_{f}\leqslant I\cdot{h}_{gi}{\text{，}}{g\in {g}_{i}}{\text{，}}\\ {g}_{j}\notin {g}_{i}{\text{，}} \\ -\infty \text{，}{h}_{gj}\cdot{N}_{f} > I\cdot{h}_{gi}{\text{。}} \end{array}\right.$ (12)

2 实验分析

S港船舶交通流密集且航道窄小，具有多个分支航道，这些航道交叉分布，使得船舶在交汇处会遇频繁且易发生碰撞事故。本文以S港的交汇水域为研究对象，验证本文方法的船舶港口避撞路径跟踪控制效果。为了验证鱼群算法的自适应性和收敛速率，采用图1所示的栅格，随机产生100个障碍，设船舶从栅格的A点出发，到达目标点B，鱼群规模为20，迭代30次，拥挤度因子为0.8。

 图 2 船舶避撞路径图 Fig. 2 Collision avoidance path diagram of ship

 图 3 动态障碍碰撞过程 Fig. 3 Dynamic obstacle collision process

3 结　语

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