Neural-network based AUV path planning in estuary environments

Shuai Li, Yi Guo

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

19 Citations (Scopus)


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
Number of pages7
Publication statusPublished - 1 Dec 2012
Externally publishedYes
Event10th World Congress on Intelligent Control and Automation, WCICA 2012 - Beijing, China
Duration: 6 Jul 20128 Jul 2012


Conference10th World Congress on Intelligent Control and Automation, WCICA 2012


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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications


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