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Combining Evolution and Training in a Robotic Controller for Autonomous Vehicle Navigation

  • Jefferson Rodrigo de Souza
  • Gustavo Pessin
  • Fernando Santos Osório
  • Denis Fernando Wolf
  • Patrícia Amâncio Vargas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7429)

Abstract

This work investigates a robotic control system designed to autono-mously navigate a vehicle in urban environments. Our approach is based on the use of two Artificial Neural Networks (ANNs), one is trained for image processing with road recognition and template matching and the second is evolved for navigation control. This paper focuses on experiments and evaluations using a Genetic Algorithm (GA) to evolve the ANN responsible to provide steering and velocity control to the autonomous vehicle.

References

  1. 1.
    Shinzato, P.Y., Wolf, D.F.: A road following approach using artificial neural networks combinations. Journal of Intelligent and Robotic Systems 62(3), 527–546 (2010)CrossRefGoogle Scholar
  2. 2.
    Souza, J.R., Pessin, G., Eboli, G.B., Mendes, C.C.T., Osório, F.S., Wolf, D.F.: Vision and gps-based autonomous vehicle navigation using templates and artificial neural networks. In: The 27th ACM Symposium on Applied Computing, pp. 1008–1013 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jefferson Rodrigo de Souza
    • 1
  • Gustavo Pessin
    • 1
    • 2
  • Fernando Santos Osório
    • 1
  • Denis Fernando Wolf
    • 1
  • Patrícia Amâncio Vargas
    • 2
  1. 1.Institute of Mathematics and Computer Science (ICMC)University of São Paulo (USP)São CarlosBrazil
  2. 2.School of Mathematical and Computer Sciences (MACS)Heriot-Watt UniversityEdinburghUK

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