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Applied Bionics and Biomechanics
Volume 7, Issue 1, Pages 57-67
http://dx.doi.org/10.1080/11762320903244843

Controlling Underwater Robots with Electronic Nervous Systems

Joseph Ayers,1 Nikolai Rulkov,2 Dan Knudsen,3 Yong-Bin Kim,4 Alexander Volkovskii,5 and Allen Selverston5

1Department of Biology and Marine Science Center, Northeastern University, East Point, Nahant, MA 01908, USA
2Information Systems Laboratories, Inc., 10070 Barnes Canyon Road, San Diego CA 92121, USA
3Marine Science Center, Northeastern University, East Point, Nahant, MA 01908, USA
4Department of Electrical and Computer Engineering, Northeastern University, 360 Huntington Ave. Boston, MA 02115, USA
5Institute for Nonlinear Science-0402, UCSD, La Jolla, CA 92093-0402, USA

Received 10 August 2009

Copyright © 2010 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We are developing robot controllers based on biomimetic design principles. The goal is to realise the adaptive capabilities of the animal models in natural environments. We report feasibility studies of a hybrid architecture that instantiates a command and coordinating level with computed discrete-time map-based (DTM) neuronal networks and the central pattern generators with analogue VLSI (Very Large Scale Integration) electronic neuron (aVLSI) networks. DTM networks are realised using neurons based on a 1-D or 2-D Map with two additional parameters that define silent, spiking and bursting regimes. Electronic neurons (ENs) based on Hindmarsh–Rose (HR) dynamics can be instantiated in analogue VLSI and exhibit similar behaviour to those based on discrete components. We have constructed locomotor central pattern generators (CPGs) with aVLSI networks that can be modulated to select different behaviours on the basis of selective command input. The two technologies can be fused by interfacing the signals from the DTM circuits directly to the aVLSI CPGs. Using DTMs, we have been able to simulate complex sensory fusion for rheotaxic behaviour based on both hydrodynamic and optical flow senses. We will illustrate aspects of controllers for ambulatory biomimetic robots. These studies indicate that it is feasible to fabricate an electronic nervous system controller integrating both aVLSI CPGs and layered DTM exteroceptive reflexes.