Abstract
In this paper, the reflexive behavior of a biomorphic adaptive robot is analyzed. The motion generation of the robot is governed by a Reaction-Diffusion Cellular Neural Network (RD-CNN) that evolves towards a Turing pattern representing the action pattern of the robot. The initial conditions of this RD-CNN are given by the sensor input. The proposed approach is particularly valuable when the number of sensors is high, being able to perform data compression in real-time through analog parallel processing. An experiment using a small 6-legged robot realized in Lego MindStorms™ with three sensors is presented to validate the approach. A simulated 3×3 CNN is used to control this hexapod.
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© 2008 CISM, Udine
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Pavone, M., Stich, M., Streibl, B. (2008). Locomotion control of a hexapod by Turing patterns. In: Arena, P. (eds) Dynamical Systems, Wave-Based Computation and Neuro-Inspired Robots. CISM International Centre for Mechanical Sciences, vol 500. Springer, Vienna. https://doi.org/10.1007/978-3-211-78775-5_16
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DOI: https://doi.org/10.1007/978-3-211-78775-5_16
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-78774-8
Online ISBN: 978-3-211-78775-5
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