Cooperative AUV Navigation using a Single Maneuvering Surface Craft
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Leonard_Cooperative AUV.pdf
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726.27 KB
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0914a3462380f28a35d1d13c79e18a08
Author(s)
Fallon, Maurice Francis
Papadopoulos, Georgios
Leonard, John Joseph
Patrikalakis, Nicholas M.
Date Issued
August 2010
Journal
International Journal of Robotics Research
Publisher
Sage Publications
Citation
Fallon, M. F. et al. “Cooperative AUV Navigation Using a Single Maneuvering Surface Craft.” The International Journal of Robotics Research 29.12 (2010): 1461–1474.
Version
Author's final manuscript
Abstract
In this paper we describe the experimental implementation of an online algorithm for cooperative localization of submerged autonomous underwater vehicles (AUVs) supported by an autonomous surface craft. Maintaining accurate localization of an AUV is difficult because electronic signals, such as GPS, are highly attenuated by water. The usual solution to the problem is to utilize expensive navigation sensors to slow the rate of dead-reckoning divergence. We investigate an alternative approach that utilizes the position information of a surface vehicle to bound the error and uncertainty of the on-board position estimates of a low-cost AUV. This approach uses the Woods Hole Oceanographic Institution (WHOI) acoustic modem to exchange vehicle location estimates while simultaneously estimating inter-vehicle range. A study of the system observability is presented so as to motivate both the choice of filtering approach and surface vehicle path planning. The first contribution of this paper is to the presentation of an experiment in which an extended Kalman filter (EKF) implementation of the concept ran online on-board an OceanServer Iver2 AUV while supported by an autonomous surface vehicle moving adaptively. The second contribution of this paper is to provide a quantitative performance comparison of three estimators: particle filtering (PF), non-linear least-squares optimization (NLS), and the EKF for a mission using three autonomous surface craft (two operating in the AUV role). Our results indicate that the PF and NLS estimators outperform the EKF, with NLS providing the best performance.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1177/0278364910380760
