QUT ePrints

Trajectory design for autonomous underwater vehicles based on ocean model predictions for feature tracking

Smith, Ryan N., Chao, Yi, Jones, Burton H., Caron, David A., Li, Peggy P., & Sukhatme, Gaurav S. (2009) Trajectory design for autonomous underwater vehicles based on ocean model predictions for feature tracking. In Proceedings of The 7th International Conference on Field and Service Robotics, Springer Berlin/Heidelberg, MIT, Cambridge, MA, pp. 263-273.

View at publisher

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.

Impact and interest:

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

140 since deposited on 27 Mar 2011
48 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 40130
Item Type: Conference Paper
Additional URLs:
Keywords: Autonomous Underwater Vehicle, Ocean modeling, Algal bloom, Trajectory design, Path Planning
Subjects: Australian and New Zealand Standard Research Classification > EARTH SCIENCES (040000) > OCEANOGRAPHY (040500) > Biological Oceanography (040501)
Australian and New Zealand Standard Research Classification > EARTH SCIENCES (040000) > OCEANOGRAPHY (040500) > Physical Oceanography (040503)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MARITIME ENGINEERING (091100) > Ocean Engineering (091103)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MARITIME ENGINEERING (091100) > Special Vehicles (091106)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2009 Springer
Copyright Statement: This is the author-version of the work. Conference proceedings published, by Springer Verlag, will be available via SpringerLink. http://www.springerlink.com
Deposited On: 28 Mar 2011 08:39
Last Modified: 11 Aug 2011 03:41

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page