Robot vision with cellular neural networks: a practical implementation of new algorithms: Research Articles

Authors:
Giovanni Egidio Pazienza;Xavier Ponce-García;Marco Balsi;Xavier Vilasís-Cardona
Affiliations:
Departament d'Electrònica, Enginyeria i Arquitectura La Salle, Universitat ‘Ramon Llull’, Pg. Bonanova 8, Barcelona 08022, Spain;Departament d'Electrònica, Enginyeria i Arquitectura La Salle, Universitat ‘Ramon Llull’, Pg. Bonanova 8, Barcelona 08022, Spain;Dipartimento di Ingegneria Elettronica, Università ‘La Sapienza’, via Eudossiana 18, Roma 00184, Italy;Departament d'Electrònica, Enginyeria i Arquitectura La Salle, Universitat ‘Ramon Llull’, Pg. Bonanova 8, Barcelona 08022, Spain
Venue:
International Journal of Circuit Theory and Applications
Year:
2007

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Abstract

Cellular neural networks (CNNs) are well suited for image processing due to the possibility of a parallel computation. In this paper, we present two algorithms for tracking and obstacle avoidance using CNNs. Furthermore, we show the implementation of an autonomous robot guided using only real-time visual feedback; the image processing is performed entirely by a CNN system embedded in a digital signal processor (DSP). We successfully tested the two algorithms on this robot. Copyright © 2006 John Wiley & Sons, Ltd.