Neuro-Evolution of Mobile Robot Controller

  • Ivan Sekaj Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Slovakia https://orcid.org/0000-0002-3359-0110
  • Ladislav Cíferský Institute of Informatics and Mathematics s, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Slovakia
  • Milan Hvozdík Institute of Informatics and Mathematics s, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Slovakia
DOI: https://doi.org/10.13164/mendel.2019.1.039
Keywords: mobile robot, 2D environment, neural network, evolutionary-based learning, genetic algorithm

Abstract

We present a neuro-evolution design for control of a mobile robot in 2D simulation environment. The mobile robot is moving in unknown environment with obstacles from the start position to the goal position. The trajectory of the robot is controlled by a neural network – based controller which inputs are information from several laser beam sensors. The learning of the neural network controller is based on an evolutionary approach, which is provided by genetic algorithm.

References

Siciliano, B. and Khatib, O. 2008. Springer handbook of robotics, Springer-Verlag Berlin Heidelberg.

Duchoň, F., Babinec, A., Kajan, M., Beňo, P., Florek, M., Fico, T., and Jurišica, L. 2014. Path planning with modified a star algorithm for a mobile robot. Procedia Engineering, 96, pp. 59–69.

Babinec, A., Duchoň, F., Dekan, M., Pásztó, P., and Kelemen, M. VFH* TDT (VFH* with Time Dependent Tree): A new laser rangefinder based obstacle avoidance method designed for environment with non-static obstacles. Robotics and autonomous systems, 62, 8, pp. 1098–1115.

Duchoň, F., Huňady, D., Dekan, M., and Babinec, A. 2013. Optimal navigation for mobile robot in known environment. Applied Mechanics and Materials 282, pp. 33–38.

Michalewicz, Z. 1996. Genetic Algorithms + Data Structures = Evolutionary Programs. Springer-Verlag Berlin Heidelberg.

Eiben, A. E. and Smith, J. E. 2003. Introduction to Evolutionary Computing. Springer-Verlag Berlin Heidelberg.

Matlab R2017b. 2017. The MathWorks, Inc. www.mathworks.com

Published
2019-06-24
How to Cite
[1]
Sekaj, I., Cíferský, L. and Hvozdík, M. 2019. Neuro-Evolution of Mobile Robot Controller. MENDEL. 25, 1 (Jun. 2019), 39-42. DOI:https://doi.org/10.13164/mendel.2019.1.039.
Issue
Vol 25 No 1 (2019): MENDEL
Section
Research articles

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