Neural-network based AUV path planning in estuary environments

Shuai Li; Yi Guo

doi:10.1109/WCICA.2012.6359093

Neural-network based AUV path planning in estuary environments

Shuai Li, Yi Guo

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

23 Citations (Scopus)

Abstract

For the path planning problem of autonomous underwater vehicles (AUVs) in 3-dimensional (3-D) estuary environments, traditional methods may encounter problems due to their high computational complexity. In this paper, we proposed a dynamic neural network to solve the AUV path planning problem. In the neural network, neurons get input from the environment, locally interact with the neighbors and update neural activities in real time. The AUV path is then generated according to the neural activity landscapes. Stability, computational complexity of the neural network, and optimality of the generated path are analyzed. AUV path planning in 3-D complex environments without currents, with constant currents, and with variable currents are studied through simulations, which demonstrate the effectiveness of this approach.

Original language	English
Title of host publication	WCICA 2012 - Proceedings of the 10th World Congress on Intelligent Control and Automation
Pages	3724-3730
Number of pages	7
DOIs	https://doi.org/10.1109/WCICA.2012.6359093
Publication status	Published - 1 Dec 2012
Externally published	Yes
Event	10th World Congress on Intelligent Control and Automation, WCICA 2012 - Beijing, China Duration: 6 Jul 2012 → 8 Jul 2012

Conference

Conference	10th World Congress on Intelligent Control and Automation, WCICA 2012
Country/Territory	China
City	Beijing
Period	6/07/12 → 8/07/12

Keywords

autonomous underwater vehicle
estuary environments
Neural networks
path planning

ASJC Scopus subject areas

Control and Systems Engineering
Software
Computer Science Applications

More information

10.1109/WCICA.2012.6359093

Cite this

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title = "Neural-network based AUV path planning in estuary environments",

abstract = "For the path planning problem of autonomous underwater vehicles (AUVs) in 3-dimensional (3-D) estuary environments, traditional methods may encounter problems due to their high computational complexity. In this paper, we proposed a dynamic neural network to solve the AUV path planning problem. In the neural network, neurons get input from the environment, locally interact with the neighbors and update neural activities in real time. The AUV path is then generated according to the neural activity landscapes. Stability, computational complexity of the neural network, and optimality of the generated path are analyzed. AUV path planning in 3-D complex environments without currents, with constant currents, and with variable currents are studied through simulations, which demonstrate the effectiveness of this approach.",

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language = "English",

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pages = "3724--3730",

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note = "10th World Congress on Intelligent Control and Automation, WCICA 2012 ; Conference date: 06-07-2012 Through 08-07-2012",

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TY - GEN

T1 - Neural-network based AUV path planning in estuary environments

AU - Li, Shuai

AU - Guo, Yi

PY - 2012/12/1

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N2 - For the path planning problem of autonomous underwater vehicles (AUVs) in 3-dimensional (3-D) estuary environments, traditional methods may encounter problems due to their high computational complexity. In this paper, we proposed a dynamic neural network to solve the AUV path planning problem. In the neural network, neurons get input from the environment, locally interact with the neighbors and update neural activities in real time. The AUV path is then generated according to the neural activity landscapes. Stability, computational complexity of the neural network, and optimality of the generated path are analyzed. AUV path planning in 3-D complex environments without currents, with constant currents, and with variable currents are studied through simulations, which demonstrate the effectiveness of this approach.

AB - For the path planning problem of autonomous underwater vehicles (AUVs) in 3-dimensional (3-D) estuary environments, traditional methods may encounter problems due to their high computational complexity. In this paper, we proposed a dynamic neural network to solve the AUV path planning problem. In the neural network, neurons get input from the environment, locally interact with the neighbors and update neural activities in real time. The AUV path is then generated according to the neural activity landscapes. Stability, computational complexity of the neural network, and optimality of the generated path are analyzed. AUV path planning in 3-D complex environments without currents, with constant currents, and with variable currents are studied through simulations, which demonstrate the effectiveness of this approach.

KW - autonomous underwater vehicle

KW - estuary environments

KW - Neural networks

KW - path planning

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U2 - 10.1109/WCICA.2012.6359093

DO - 10.1109/WCICA.2012.6359093

M3 - Conference article published in proceeding or book

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SP - 3724

EP - 3730

BT - WCICA 2012 - Proceedings of the 10th World Congress on Intelligent Control and Automation

T2 - 10th World Congress on Intelligent Control and Automation, WCICA 2012

Y2 - 6 July 2012 through 8 July 2012

ER -

Neural-network based AUV path planning in estuary environments

Abstract

Conference

Keywords

ASJC Scopus subject areas

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