A chaotic neural network as motor path generator for mobile robotics

Michele Folgheraiter, Giuseppina Gini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This work aims at developing a motor path generator for applications in mobile robotics based on a chaotic neural network. The computational paradigm inspired by the neural structure of microcircuits located in the human prefrontal cortex is adapted to work in real-time and used to generate the joints trajectories of a lightweight quadruped robot. The recurrent neural network was implemented in Matlab and a software framework was developed to test the performances of the system with the robot dynamic model. Preliminary results demonstrate the capability of the neural controller to learn period signals in a short period of time allowing adaptation during the robot operation.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages64-69
Number of pages6
ISBN (Electronic)9781479973965
DOIs
Publication statusPublished - Apr 20 2014
Event2014 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2014 - Bali, Indonesia
Duration: Dec 5 2014Dec 10 2014

Publication series

Name2014 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2014

Other

Other2014 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2014
Country/TerritoryIndonesia
CityBali
Period12/5/1412/10/14

Keywords

  • Control Path Generator
  • Dynamic Neural Network
  • Lightweight Quadruped Robot
  • Neurodynamics
  • Recurrent Neural Network RNN

ASJC Scopus subject areas

  • Biotechnology
  • Artificial Intelligence
  • Human-Computer Interaction

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