Developing Fleets of Autonomous Underwater Vehicles

Abstract

Autonomous underwater vehicles (AUVs) have a demonstrated capability to collect valuable data for scientific and military purposes. Historically, individual vehicles have been used. To reduce the overall time and cost of acquiring data over large areas, multiple vehicles must be used. A fleet of 5 AUVs, capable of underwater commendation, were fabricated. Languages and logics were developed to enable collaborative operations among the vehicles. Experiments with a formation of 5 AUVs operating underwater simultaneously are described. The AUVs operated autonomously, in that they enabled their operations on their own, initiated and constrained by underwater acoustic communication and navigation against a general behavioral background provided by programmed logics. The operations were not choreographed in advance and programmed into the machines, nor were they the result of intervention by an operator on the surface. The vehicles performed deployment, formation-flying, vehicle replacement, divert-to-point of interest, and leader replacement behaviors. The experiments show that autonomous collaborative behavior by 5 AUVs is possible under the constraints of underwater acoustic navigation and acoustic communication.

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 2008
Accession Number
ADA478903

Entities

People

  • Dean B. Edwards

Organizations

  • University of Idaho

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Acoustic Communications
  • Acoustic Navigation
  • Algorithms
  • Autonomous Underwater Vehicles
  • Autonomous Vehicles
  • Communication Networks
  • Communication Systems
  • Computer Programs
  • Control Systems
  • Detectors
  • Language
  • Measurement
  • Navigation
  • Underwater Acoustic Communications
  • Underwater Communications
  • Underwater Vehicles
  • Vehicles

Readers

  • Acoustical Oceanography.
  • Maritime Combat Support and Expeditionary Logistics.
  • Neural Network Machine Learning.