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Snookie: An Autonomous Underwater Vehicle with Artificial Lateral-Line System

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Flow Sensing in Air and Water

Abstract

In this work we present Snookie, an autonomous underwater vehicle with an artificial lateral-line system. Integration of the artificial lateral-line system with other sensory modalities is to enable the robot to perform behaviors as observed in fish, such as obstacle detection and geometrical-shape reconstruction by means of hydrodynamic images. The present chapter consists of three sections devoted to design of the robot, its lateral-line system, and processing of the ensuing flow-sensory data. The artificial lateral-line system of Snookie is presented in detail, together with a simple version of a flow reconstruction algorithm applicable to both the artificial lateral-line system and, e.g. the blind Mexican cave fish. More in particular, the first section deals with the development of the autonomous underwater vehicle Snookie, which provides the functionality and is tailored to the requirements of the artificial lateral-line system. The second section is devoted to the implementation of the artificial lateral-line system that consists of an array of hot thermistor anemometers to be integrated in the nozzle. In the final section, the information processing ensuing from the flow sensors and leading to conclusions about the environment is presented. The measurement of the tangential velocities at the artificial lateral-line system together with the no-penetration condition provides the robot with Cauchy boundary conditions, so that the hydrodynamic mapping of potential flow onto the lateral line can be inverted. Through this inversion information is accessible from the flow around the artificial lateral line about objects in the neighbourhood, which alter the flow field.

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Abbreviations

2D:

2-dimensional

3D:

3-dimensional

6D:

6-dimensional

ALL:

Artificial lateral-line system

AUV:

Autonomous underwater vehicle

BEM:

Boundary-element method

BFS:

Body-fixed system

CAD:

Computer-aided design

FOR:

Frame of reference

PD:

Proportional-derivative

PVDF:

Polyvinylidene fluoride fibers

PWM:

Pulse-width modulation

SLAM:

Simultaneous localisation and mapping

TFR:

Thikonov-regularised flow-field reconstruction

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Acknowledgments

This work was supported in part by the DFG excellence initiative research cluster Cognition for Technical Systems (CoTeSys)—see www.cotesys.org—and the Bernstein Center for Computational Neuroscience (BCCN) Munich—see www.bccn-munich.de. We thank all students, who have participated in this project.

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Authors and Affiliations

  1. Physik Department, Lehrstuhl für Theoretische Biophysik, T35, TU München, James-Franck-Straße, 85748, Garching, Germany

    Andreas N. Vollmayr, Sebastian Urban & J. Leo van Hemmen

  2. Lehrstuhl für Informationstechnische Regelung, Fakultät für Elektro- und Informationstechnik, TU München, Barer Straße 21, 80333, München, Germany

    Stefan Sosnowski & Sandra Hirche

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  1. Andreas N. Vollmayr
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  2. Stefan Sosnowski
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  3. Sebastian Urban
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  5. J. Leo van Hemmen
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Correspondence to Andreas N. Vollmayr .

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Editors and Affiliations

  1. Institute for Zoology, University of Bonn, Bonn, Germany

    Horst Bleckmann

  2. Institute for Zoology, University of Bonn, Bonn, Germany

    Joachim Mogdans

  3. Department of Biological Sciences, Bowling Green State University, Bowling Green, Ohio, USA

    Sheryl L. Coombs

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Vollmayr, A.N., Sosnowski, S., Urban, S., Hirche, S., van Hemmen, J.L. (2014). Snookie: An Autonomous Underwater Vehicle with Artificial Lateral-Line System. In: Bleckmann, H., Mogdans, J., Coombs, S. (eds) Flow Sensing in Air and Water. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41446-6_20

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