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Article Abstract – Lin et al. (2014)


A Multi-AUV State Estimator for Determining the 3D Position of Tagged Fish

Authors and affiliations:

Lin, Y.1, H. Kastein2, T. Peterson2, C. White3, C. Lowe3, and C. Clark2

1Department of Computer Science, Harvey Mudd College
2Department of Engineering, Harvey Mudd College
3Department of Biological Sciences, California State University, Long Beach


2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014)


This paper presents a multi-AUV state-estimator that can determine the 3D position of a tagged fish. In addition to angle measurements, the state-estimator also incorporates distance and depth measurements. These additional sensor measurements allow for greater accuracy in the position estimates. A newly developed motion model that better accounts for multiple hypotheses of the motion of a tagged fish is used to increase the robustness of the state-estimator. A series of multi-AUV shark tracks were conducted at Santa Catalina Island, California over the span of four days to demonstrate the ability of the state-estimator to determine the 3D position of a tagged leopard shark. Additional experiments in which the AUVs tracked a tagged boat of known location were conducted to quantify the performance of the presented state-estimator. Experimental results demonstrate a three-fold decrease in mean state-estimation error compared to previous works.

Full text:

PDF (from Clark lab)

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