Optimal Time-Consuming Path Planning for Autonomous Underwater Vehicles Based on a Dynamic Neural Network Model in Ocean Current Environments

Path planning is a prerequisite for autonomous underwater vehicles to perform tasks autonomously. Many shortest distance algorithms are applied, and ocean currents are ignored to plan a short path in distance, which is usually time and energy consuming. In fact, the favourable currents can be exploited while avoiding the opposite ocean flows. Based on the bioinspired neural network architecture, this paper proposes a novel dynamic neural network model to plan the time-saving path in ocean current environments. After that, the path is smoothed by the B-spline algorithm. Analysis of the model shows that it can find out the minimum time path. Many simulations have also been introduced to test the effectiveness of the proposed model, showing good results. The dynamic neural network model has no learning procedure and can run in parallel. It has the advantages of loose parameter restrictions and wide spreading of neural activities. In addition, it has also been proven to be suitable for strong ocean currents.

• Record URL:
  http://dx.doi.org/10.1109/TVT.2020.3034628
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2020, IEEE.
• Authors:
  • Chen, Mingzhi
  • Zhu, Daqi
• Publication Date: 2020-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 14401-14412
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 69
  • Issue Number: 12
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Algorithms; Heuristic methods; Neural networks; Ocean currents; Trajectory control; Unmanned underwater vehicles; Vehicle dynamics
• Subject Areas: Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01770893
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 29 2021 5:28PM