Cargando…

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there ar...

Descripción completa

Detalles Bibliográficos
Autores principales: Ni, Jianjun, Wu, Liuying, Shi, Pengfei, Yang, Simon X.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5309431/
https://www.ncbi.nlm.nih.gov/pubmed/28255297
http://dx.doi.org/10.1155/2017/9269742
_version_ 1782507703371825152
author Ni, Jianjun
Wu, Liuying
Shi, Pengfei
Yang, Simon X.
author_facet Ni, Jianjun
Wu, Liuying
Shi, Pengfei
Yang, Simon X.
author_sort Ni, Jianjun
collection PubMed
description Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.
format Online
Article
Text
id pubmed-5309431
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Hindawi Publishing Corporation
record_format MEDLINE/PubMed
spelling pubmed-53094312017-03-02 A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles Ni, Jianjun Wu, Liuying Shi, Pengfei Yang, Simon X. Comput Intell Neurosci Research Article Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently. Hindawi Publishing Corporation 2017 2017-02-01 /pmc/articles/PMC5309431/ /pubmed/28255297 http://dx.doi.org/10.1155/2017/9269742 Text en Copyright © 2017 Jianjun Ni et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ni, Jianjun
Wu, Liuying
Shi, Pengfei
Yang, Simon X.
A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles
title A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles
title_full A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles
title_fullStr A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles
title_full_unstemmed A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles
title_short A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles
title_sort dynamic bioinspired neural network based real-time path planning method for autonomous underwater vehicles
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5309431/
https://www.ncbi.nlm.nih.gov/pubmed/28255297
http://dx.doi.org/10.1155/2017/9269742
work_keys_str_mv AT nijianjun adynamicbioinspiredneuralnetworkbasedrealtimepathplanningmethodforautonomousunderwatervehicles
AT wuliuying adynamicbioinspiredneuralnetworkbasedrealtimepathplanningmethodforautonomousunderwatervehicles
AT shipengfei adynamicbioinspiredneuralnetworkbasedrealtimepathplanningmethodforautonomousunderwatervehicles
AT yangsimonx adynamicbioinspiredneuralnetworkbasedrealtimepathplanningmethodforautonomousunderwatervehicles
AT nijianjun dynamicbioinspiredneuralnetworkbasedrealtimepathplanningmethodforautonomousunderwatervehicles
AT wuliuying dynamicbioinspiredneuralnetworkbasedrealtimepathplanningmethodforautonomousunderwatervehicles
AT shipengfei dynamicbioinspiredneuralnetworkbasedrealtimepathplanningmethodforautonomousunderwatervehicles
AT yangsimonx dynamicbioinspiredneuralnetworkbasedrealtimepathplanningmethodforautonomousunderwatervehicles