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Article Abstract – Smith et al. (2016)


Stochastic modeling and control for tracking the periodic movement of marine animals via AUVs

Authors and affiliations:

Kevin D. Smith1, Shih-Chieh Hsiung2, Connor White3, Christopher G. Lowe3, and Christopher Clark4

1Department of Physics, Harvey Mudd College
2Department of Computer Science, Harvey Mudd College
3Department of Biological Sciences, California State University Long Beach
4Department of Engineering, Harvey Mudd College


IEEE International Conference on Intelligent Robots and Systems (IROS), 2016.


This paper presents a graph-based model of periodic migrations of tagged fish populations and two multi-AUV stochastic controllers developed to track these fish from the model. The model presented in this paper characterizes patterns in the historical movement of tagged fish and is used to develop stochastic tracking by a “model based control” and a “feedback control”. These two controllers permit swarms of AUVs to track the transition probabilities of the tagged population between vertices of the model. To validate these controllers, a periodic model is developed for a simulated population based on three months of geolocation data from a kelp bass (Paralabrax clathratus), and AUV teams utilizing both controllers are simulated in tracking this population. Results show the viability of stochastic controls for multi-AUV tracking of populations whose behavior is well-approximated by the graph-based model. Preliminary trials with physical AUV systems indicate the plausibility of hardware implementation.

Full text:

PDF (from Clark lab)

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