An Improved Artificial Potential Field Method for Ship Path Planning Based on Artificial Potential Field—Mined Customary Navigation Routes

Author:

Suo Yongfeng1,Chen Xinyu1ORCID,Yue Jie1,Yang Shenhua1,Claramunt Christophe2ORCID

Affiliation:

1. Navigation College, Jimei University, Xiamen 361021, China

2. Naval Academy Research Institute, 29240 Brest, France

Abstract

In recent years, the artificial potential field has garnered significant attention in ship route planning and traffic flow simulation. However, the traditional artificial potential field method faces challenges in accurately simulating a ship’s customary route and navigating experience, leading to significant deviations in prediction results. To address these issues, in this study, we propose an innovative method for simulating and predicting ship traffic flow, building upon the artificial potential field approach. We introduce an AIS track heat map based on the kernel density function and enhance the artificial potential field model by incorporating factors, such as ship navigation habits and ship size. Through a comparison of traffic flow changes before and after the construction of a wind farm, the optimized model demonstrates its effectiveness in improving the accuracy of prediction results.

Publisher

MDPI AG

Reference24 articles.

1. A hierarchical methodology for vessel traffic flow prediction using Bayesian tensor decomposition and similarity grouping;Wenbin;Ocean Eng.,2023

2. Deep spatio-temporal 3D dilated dense neural network for traffic flow prediction;Rui;Expert Syst. Appl.,2024

3. STHSGCN: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction;Yu;Heliyon,2023

4. Dynamic multi-graph neural network for traffic flow prediction incorporating traffic accidents;Ye;Expert Syst. Appl.,2023

5. Generic Dynamic Graph Convolutional Network for traffic flow forecasting;Yi;Inf. Fusion,2023

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Applications of Voronoi Diagrams in Multi-Robot Coverage: A Review;Journal of Marine Science and Engineering;2024-06-19

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3