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Content Provider | IEEE Xplore Digital Library |
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Author | Smith, R.N. Pereira, A. Yi Chao Li, P.P. Caron, D.A. Jones, B.H. Sukhatme, G.S. |
Copyright Year | 2010 |
Description | Author affiliation: Jet Propulsion Laboratory, California Institute of Technology 4800 Oak Grove Drive, Pasadena, 91109, USA (Yi Chao; Li, P.P.) || Robotic Embedded Systems Laboratory, Department of Computer Science, University of Southern California, Los Angeles, 90089, USA (Smith, R.N.; Pereira, A.; Sukhatme, G.S.) || Department of Marine Biology, University of Southern California, Los Angeles, 90089, USA (Caron, D.A.; Jones, B.H.) |
Abstract | Data collection using Autonomous Underwater Vehicles (AUVs) is increasing in importance within the oceanographic research community. Contrary to traditional moored or static platforms, mobile sensors require intelligent planning strategies to maneuver 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. |
Starting Page | 4770 |
Ending Page | 4777 |
File Size | 1654813 |
Page Count | 8 |
File Format | |
ISBN | 9781424450381 |
ISSN | 10504729 |
DOI | 10.1109/ROBOT.2010.5509240 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-05-03 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Underwater vehicles Trajectory Oceans Predictive models Intelligent sensors Sea measurements Path planning Strategic planning Navigation Marine vehicles |
Content Type | Text |
Resource Type | Article |
Subject | Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software |
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