Study on path planning of unmanned surface vessel based on data-driven genetic algorithm

Junfeng XIN; Yongbo ZHANG; Jiageng BO; Bowen ZHAO; Shiyuan FAN

doi:10.11959/j.issn.2096-6652.201926

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Study on path planning of unmanned surface vessel based on data-driven genetic algorithm

Regular Papers | 更新时间：2024-06-05

- Study on path planning of unmanned surface vessel based on data-driven genetic algorithm
- Chinese Journal of Intelligent Science and Technology Vol. 1, Issue 2, Pages: 171-180(2019)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.201926
  CLC： TH212，TH213.3
- Published Online：2019-06，
  
  Published：20 June 2019
- 稿件说明：
移动端阅览
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：

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

GOLDBERG D E . Genetic algorithms in search,optimization and machine learning [M ] . Addison-Wesley Pub.Co . 1989 ： 2104 - 2116 .

SRINIVAS M , PATNAIK L M . Adaptive probabilities of crossover and mutation in genetic algorithms [J ] . IEEE Transactions on Systems Man ＆ Cybernetics , 2002 , 24 ( 4 ): 656 - 667 .

郑南宁 . 人工智能新时代 [J ] . 智能科学与技术学报 , 2019 , 1 ( 1 ): 1 - 3 .

ZHENG N N . The new era of artificial intelligence [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 1 ): 1 - 3 .

张钹 . 人工智能进入后深度学习时代.智能科学与技术学报 [J ] . 2019 , 1 ( 1 ): 4 - 6 .

ZHANG B . Artificial intelligence is entering the post deep-learning era [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 1 ): 4 - 6 .

郑亚君 , 薛磊 , 董璐 , 等 . 基于 UWB 定位技术的多移动机器人编队控制.智能科学与技术学报 [J ] . 2019 , 1 ( 1 ): 83 - 87 .

ZHENG Y J , XUE L , DONG L , et al . Formation control of mobile robots with UWB localization technology [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 1 ): 83 - 87 .

CHANG W A , RAMAKRISHNA R S . A genetic algorithm for shortest path routing problem and the sizing of populations [M ] . IEEE Press , 2002 .

HAUPT R L , HAUPT S E . Practical Genetic Algorithms,Second Edition [M ] . 2004 .

TSAI C C , HUANG H C , CHAN C K . Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation [J ] . IEEE Transactions on Industrial Electronics , 2011 , 58 ( 10 ): 4813 - 4821 .

TUNCER A , YILDIRIM M . Dynamic path planning of mobile robots with improved genetic algorithm [J ] . Computers ＆ Electrical Engineering , 2012 , 38 ( 6 ): 1564 - 1572 .

ELSHAMLI A , ABDULLAH H A , AREIBI S . Genetic algorithm for dynamic path planning [C ] // Canadian Conference on Electrical and Computer Engineering . 2004 : 677 - 680 .

MANELY J E , . Unmanned surface vehicles,15 years of development [C ] // Proc.Oceans 2008 MTS/IEEE Quebec Conference Exhibition . 2008 : 1 - 4 .

VEERS J , BERTRAN V . Development of the USV multi-mission surface vehicle III [C ] // 5th Int.Computer and IT Application in the Maritime Industries . 2006 : 345 - 355 .

LU Y S . Research on the path planning algorithm for the surface of the surface [D ] . Harbin:Harbin Engineering University , 2010 .

RATNAWEERA A , HALGAMUGE S K , WATSON H C . Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients [J ] . IEEE Transactions on Evolutionary Computation , 2004 , 8 ( 3 ): 240 - 255 .

HOLLAND J H . Adaptation in natural and artificial systems [J ] . Quarterly Review of Biology , 1975 , 6 ( 2 ): 126 - 137 .

LIANG J Y . Some common problems in the application of genetic algorithm [J ] . Computer Application Research , 1999 ( 7 ): 20 - 21 .

HOLLAND J H . Outline for a logic theory of adaptive systems [J ] . Journal of the Association for Computing Machinery , 1962 , 9 ( 3 ): 297 - 314 .

SAMADI M , OTHMAN M F . Global path planning for autonomous mobile robot using genetic algorithm [C ] // International Conference on Signal-Image Technology ＆ Internet-Based Systems . 2013 : 726 - 730 .

LEE J , KIM D W . An effective initialization method for genetic algorithm-based robot path planning using a directed acyclic graph [J ] . Information Sciences , 2015 :332.

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