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

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

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)263-273
Number of pages11
JournalSpringer Tracts in Advanced Robotics
Volume62
StatePublished - Jan 1 2010

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Trajectory Design for Autonomous Underwater Vehicles Based on Ocean Model Predictions for Feature Tracking'. Together they form a unique fingerprint.

Cite this