基于数据驱动的遗传算法的无人艇路径规划研究

辛峻峰; 张永波; 伯佳更; 赵博文; 范世缘

doi:10.11959/j.issn.2096-6652.201926

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基于数据驱动的遗传算法的无人艇路径规划研究

常规论文 | 更新时间：2024-06-05

- 基于数据驱动的遗传算法的无人艇路径规划研究
- Study on path planning of unmanned surface vessel based on data-driven genetic algorithm
- 智能科学与技术学报 2019年1卷第2期页码：171-180
- 作者机构：
  
  1. 青岛科技大学，山东青岛 266100
  2. 青岛国家海洋科学研究中心，山东青岛 266071
- 作者简介：
  
  [ "辛峻峰（1982- ），男，山东青岛人，中国海洋大学博士生，主要研究方向为智能无人艇设计研究、港口航道及近海工程、船舶与海洋工程等。" ]
  [ "张永波（1981- ），男，山东烟台人，中国海洋大学博士生，主要研究方向为海洋结构物水动力分析。" ]
  [ "伯佳更（1995- ），男，主要研究方向为智能无人艇路线规划研究。" ]
  [ "赵博文（1998- ），男，山东济南人，主要研究方向为船舶性能在CFD中的应用。" ]
  [ "范世缘（1998- ），女，山东济南人，主要研究方向为机械设计、神经网络、认知神经科学等。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(51609120);山东省教育厅基金资助项目;Shandong Provincial Department of Education Project(J16LB72);山东省重点研发计划基金资助项目;Shandong Key Research and Development Plan(2018YFJH0704)
- DOI：10.11959/j.issn.2096-6652.201926
  中图分类号： TH212，TH213.3
- 网络出版日期：2019-06，
  
  纸质出版日期：2019-06-20
- 稿件说明：
移动端阅览
辛峻峰, 张永波, 伯佳更, 等. 基于数据驱动的遗传算法的无人艇路径规划研究[J]. 智能科学与技术学报, 2019,1(2):171-180.

Junfeng XIN, Yongbo ZHANG, Jiageng BO, et al. Study on path planning of unmanned surface vessel based on data-driven genetic algorithm[J]. Chinese journal of intelligent science and technology, 2019, 1(2): 171-180.
辛峻峰, 张永波, 伯佳更, 等. 基于数据驱动的遗传算法的无人艇路径规划研究[J]. 智能科学与技术学报, 2019,1(2):171-180. DOI： 10.11959/j.issn.2096-6652.201926.

Junfeng XIN, Yongbo ZHANG, Jiageng BO, et al. Study on path planning of unmanned surface vessel based on data-driven genetic algorithm[J]. Chinese journal of intelligent science and technology, 2019, 1(2): 171-180. DOI： 10.11959/j.issn.2096-6652.201926.

摘要

遗传算法（GA）是无人艇路径规划系统中的一种有效方法，为了克服该算法易陷入局部最优早熟和收敛速度慢等缺陷，在不增加算法复杂度的前提下，基于数据驱动线性动态交叉策略提出了一种能够在最短时间内自适应动态调整控制参数的改进遗传算法（LCPGA）。与传统的遗传算法相比，LCPGA增加了种群多样性，能更有效地避免陷入局部最优，并提高了路径规划的精度、稳健性和收敛速度。仿真实验和无人艇现场试验验证了该算法具有更优良的性能，该算法可为无人艇路径规划提供一定的应用价值。

Abstract

The genetic algorithm (GA) is an effective method for the path planning system of unmanned surface vessel (USV)

but it is easy to fall into local optimal precocity and converges slowly.For this

without increasing the complexity of the algorithm

a data-driven linear changing parameters genetic algorithm (LCPGA) was proposed

which can adjust adaptively control parameters in the shortest time.Compared with the traditional genetic algorithm

the LCPGA increases the diversity of the population

avoids falling into local optimum more effectively

and improves the accuracy

robustness and convergence speed of path planning.Then simulation experiments and field tests verify the more excellent performance of the LCPGA.This algorithm can be helpful in path planning for unmanned surface vessel.

关键词

Keywords

references

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