Guiding a mobile robot with cellular neural networks

Xavier Vilasís-Cardona, Sonia Luengo, Jordi Solsona, Alessandro Maraschini, Giada Apicella, Marco Balsi

Research output: Indexed journal article Articlepeer-review

15 Citations (Scopus)


We show how cellular neural networks (CNNs) are capable of providing the necessary signal processing needed for visual navigation of an autonomous mobile robot. In this way, even complex feature detection and object recognition can be obtained in real time by analogue hardware, making fully autonomous real-time operation feasible. An autonomous robot was first simulated and then implemented by simulating the CNN with a DSP. The robot is capable of navigating in a maze following lines painted on the floor. Images are processed entirely by a CNN-based algorithm, and navigation is controlled by a fuzzy-rule-based algorithm.

Original languageEnglish
Pages (from-to)611-624
Number of pages14
JournalInternational Journal of Circuit Theory and Applications
Issue number6
Publication statusPublished - Nov 2002


  • Artificial vision
  • Autonomous robots
  • Cellular neural networks


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