[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3546000.3546033acmotherconferencesArticle/Chapter ViewAbstractPublication Pageshp3cConference Proceedingsconference-collections
research-article

Track planning and obstacle avoidance of wave glider based on improved artificial potential field algorithm

Published: 19 August 2022 Publication History

Abstract

The research on track planning and obstacle avoidance methods of wave glider is an indispensable ability for its smooth work in the ocean. As a real-time track planning and obstacle avoidance algorithm, artificial potential field algorithm has attracted extensive attention. Aiming at the problem that the target point can not be reached in the traditional artificial potential field algorithm, the distance factor between the wave glider and the target point is introduced into the repulsive potential field; The relative velocity potential field function is introduced and the influence of velocity potential field is enhanced to solve the threat of dynamic obstacles to wave gliders. Then, based on the motion characteristics of wave gliders, the influence of stable waves in a certain range on track planning and obstacle avoidance is analyzed. Finally, the simulation analysis is carried out. The simulation results show that this method has good track planning and obstacle avoidance effect, and the generated path is smooth. Fixed the problem of not being able to reach the target.

References

[1]
WEERAKOON T, ISHII K, NASSIRAEI A A F. An Artificial Potential Field Based Mobile Robot Navigation Method To Prevent From Deadlock[J]. Journal of Artificial Intelligence and Soft Computing Research, 2015, 5(3): 189-203.
[2]
Zhou Keshuai, Fan Pingqing. Improved A* Algorithm and Artificial Potential Field Algorithm for Mobile Robot Path Planning [J]. Electronic Devices, 2021, 44(02):368-374.
[3]
Li Xiaotao. Dynamic Modeling and Simulation research of Wave Glider [D]. China Ship Research Institute, 2014.
[4]
Xue Xuehua, Feng Hui. Global path planning of USV based on improved artificial potential field method [J]. Journal of wuhan university of technology (transportation science and engineering), 2020, 44(6):978-984.
[5]
Caio Cristiano B V, Ubiratan de Melo Pinto Junior. Adaptive Artificial Potential Fields with Orientation Control Applied to Robotic Manipulators,IFAC-PapersOnLine,Volume 53, Issue 2, 2020, Pages 9924-9929.
[6]
Pan Zhou, WAN Heng Research on Robot Path Planning Based on Improved Artificial Potential Field Method [J].Manufacturing automation, 2015, 37 (15):4-6.
[7]
Liu Zhong, Ige Obstacle Avoidance Algorithm for Unmanned Ship Based on Improved Artificial Potential Field Method [J]. Journal of Naval University of Engineering, 2021, 33(5):28-32.
[8]
Yan Xiang, GAO Junwei, Guan Sheng.Path planning of wave glider based on pattern transformation ant colony algorithm [J]. Computer engineering and applications, 2019, 55(9):100-106.
[9]
KHATIB O.Real time obstacle avoidance for ma-nipulators and mobile robots [J].International Jour-nal of Robotics and Re-search, 1986, 5(1):90-98.
[10]
Duan Jianmin, Chen Qianglong. Path planning Algorithm based on improved Artificial potential field-Genetic Algorithm. Foreign Electronic Measurement Technique, 2019, 38 (3) : 19-24.
[11]
JUNG J, PARK J S, KANG T w, Mobile robot path planning using a laser range finder for environmentswith transparent obstacles[J]. Applied Ences, 2020, 10(8) :2799.
[12]
Li Peilun, Yang Qi, etc Path planning of underwater glider based on improved artificial potential field method [J] Ship science and technology, 2019, 41 (07): 89-93
[13]
Zhi Yibo. Overview of Autonomous Obstacle Avoidance and Path Planning for Autonomous Vehicles [J]. Application of Automation, 2018(11): 128-129.
[14]
Li Huiguang, Li Xufeng, Zou Liying, Path planning of soccer robot based on artificial potential field in dynamic environment [J]. Foreign electronic measurement technique, 2008, 27(5):27-30.
[15]
Hongguang Lyu, Yong Yin.COLREGS-Constrained Real-time Path Planning for Autonomous Ships Using Modified Artificial Potential Fields[J]. The Journal ofNavigation, 2019, 72(3).

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences
HP3C '22: Proceedings of the 6th International Conference on High Performance Compilation, Computing and Communications
June 2022
221 pages
ISBN:9781450396295
DOI:10.1145/3546000
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 August 2022

Permissions

Request permissions for this article.

Check for updates

Author Tag

  1. CCS CONCEPTS • Computing methodologies • Artificial intelligence • Control methods • Motion path planning

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

HP3C'22

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 30
    Total Downloads
  • Downloads (Last 12 months)5
  • Downloads (Last 6 weeks)0
Reflects downloads up to 11 Dec 2024

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media