Trajectory Design for Autonomous Underwater Vehicles Based on Ocean Model Predictions for Feature Tracking

Abstract

Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver a vehicle to high-valued locations for data collection. We consider the use of ocean model predictions to determine the locations to be visited by an AUV, which then provides near-real time, in situ measurements back to the model to increase the skill of future predictions. The motion planning problem of steering the vehicle between the computed waypoints is not considered here. Our focus is on the algorithm to determine relevant points of interest for a chosen oceanographic feature. This represents a first approach to an end to end autonomous prediction and tasking system for aquatic, mobile sensor networks. We design a sampling plan and present experimental results with AUV retasking in the Southern California Bight (SCB) off the coast of Los Angeles.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2009
Accession Number
ADA522267

Entities

People

  • Burton H. Jones
  • David A. Caron
  • Gaurav S. Sukhatme
  • Peggy P. Li
  • Ryan N. Smith
  • Yi Chao

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Autonomous Underwater Vehicles
  • Biological Sciences
  • California
  • Control Systems
  • Electronic Mail
  • Environment
  • Fresh Water
  • Jet Propulsion
  • Microorganisms
  • Motion Planning
  • Oceans
  • Remote Sensing
  • Trajectories
  • Underwater Vehicles
  • United States
  • Vehicles

Readers

  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Robotics and Automation.