Autonomous underwater vehicle trajectory design coupled with predictive ocean models : a case study

Smith, Ryan N. , Pereira, Arvind, Chao, Yi, Li, Peggy P., Caron, David A., Jones, Burton H., & Sukhatme, Gaurav S. (2010) Autonomous underwater vehicle trajectory design coupled with predictive ocean models : a case study. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2010), IEEE Xplore, Anchorage, AK , pp. 4770-4777.

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Data collection using Autonomous Underwater Vehicles (AUVs) is increasing in importance within the oceano- graphic research community. Contrary to traditional moored or static platforms, mobile sensors require intelligent planning strategies to manoeuvre through the ocean. However, the ability to navigate to high-value locations and collect data with specific scientific merit is worth the planning efforts. In this study, we examine the use of ocean model predictions to determine the locations to be visited by an AUV, and aid in planning the trajectory that the vehicle executes during the sampling mission. The objectives are: a) to provide near-real time, in situ measurements to a large-scale ocean model to increase the skill of future predictions, and b) to utilize ocean model predictions as a component in an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. We present an algorithm designed to generate paths for AUVs to track a dynamically evolving ocean feature utilizing ocean model predictions. This builds on previous work in this area by incorporating the predicted current velocities into the path planning to assist in solving the 3-D motion planning problem of steering an AUV between two selected locations. We present simulation results for tracking a fresh water plume by use of our algorithm. Additionally, we present experimental results from field trials that test the skill of the model used as well as the incorporation of the model predictions into an AUV trajectory planner. These results indicate a modest, but measurable, improvement in surfacing error when the model predictions are incorporated into the planner.

Impact and interest:

23 citations in Scopus
14 citations in Web of Science®
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258 since deposited on 28 Mar 2011
21 in the past twelve months

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ID Code: 40123
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Autonomous Underwater Vehicle, Path Planning, Algal bloom, Ocean modeling, Feature Tracking
DOI: 10.1109/ROBOT.2010.5509240
ISBN: 9781424450381
ISSN: 1050-4729
Subjects: 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 2010 IEEE
Deposited On: 28 Mar 2011 01:24
Last Modified: 01 Mar 2012 04:04

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