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

  • Ryan N. Smith
  • Yi Chao
  • Burton H. Jones
  • David A. Caron
  • Peggy P. Li
  • Gaurav S. Sukhatme
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 62)

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 themodel 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.

Keywords

Autonomous Underwater Vehicle Domoic Acid Regional Oceanic Modeling System Trajectory Design Motion Planning Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ryan N. Smith
    • 1
  • Yi Chao
    • 2
  • Burton H. Jones
    • 3
  • David A. Caron
    • 3
  • Peggy P. Li
    • 2
  • Gaurav S. Sukhatme
    • 1
  1. 1.Robotic Embedded Systems LaboratoryUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA
  3. 3.Department of Biological SciencesUniversity of Southern CaliforniaLos AngelesUSA

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