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Control structure for a car-like robot using artificial neural networks and genetic algorithms

  • S.I. : Advances in Bio-Inspired Intelligent Systems
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Abstract

The idea of improving human’s life quality by making life more comfortable and easy is nowadays possible using current technologies and techniques to solve complex daily problems. The presented idea in this work proposes a control strategy for autonomous robotic systems, specifically car-like robots. The main objective of this work is the development of a reactive navigation controller by means of obstacles avoidance and position control to reach a desired position in an unknown environment. This research goal was achieved by the integration of potential fields and neuroevolution controllers. The neuro-evolutionary controller was designed using the (NEAT) algorithm “Neuroevolution of Augmented Topologies” and trained using a designed training environment. The methodology used allowed the vehicle to reach a certain level of autonomy, obtaining a stable controller that includes kinematic and dynamic considerations. The obtained results showed significant improvements compared to the comparison work.

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Acknowledgements

The authors wish to thank the University of Campinas and the National Council for Scientific and Technological Development (CNPq) for its support in the development of this work.

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Authors and Affiliations

  1. Mechanical Engineering School, Integrated Systems Department, Universidade Estadual de Campinas – UNICAMP, Rua Mendeleiev, 200, Campinas, São Paulo, CEP 13083-860, Brazil

    Camilo Andrés Cáceres Flórez & João Maurício Rosário

  2. Faculty of Engineering, Mechatronic Engineering Program, Universidad Militar Nueva Granada – UMNG, Carrera 11 #101-80, Bogotá, Cundinamarca, Colombia

    Dario Amaya

Authors
  1. Camilo Andrés Cáceres Flórez
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  2. João Maurício Rosário
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  3. Dario Amaya
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Correspondence to Camilo Andrés Cáceres Flórez.

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Cáceres Flórez, C.A., Rosário, J.M. & Amaya, D. Control structure for a car-like robot using artificial neural networks and genetic algorithms. Neural Comput & Applic 32, 15771–15784 (2020). https://doi.org/10.1007/s00521-018-3514-1

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