Neural-network based AUV path planning in estuary environments

Shuai Li, Yi Guo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations

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 languageEnglish
Title of host publicationWCICA 2012 - Proceedings of the 10th World Congress on Intelligent Control and Automation
Pages3724-3730
Number of pages7
DOIs
StatePublished - 2012
Event10th World Congress on Intelligent Control and Automation, WCICA 2012 - Beijing, China
Duration: 6 Jul 20128 Jul 2012

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)

Conference

Conference10th World Congress on Intelligent Control and Automation, WCICA 2012
Country/TerritoryChina
CityBeijing
Period6/07/128/07/12

Keywords

  • Neural networks
  • autonomous underwater vehicle
  • estuary environments
  • path planning

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