Mobile robot path planning using genetic algorithms

  • Carlos E. Thomaz
  • Marco Aurélio C. Pacheco
  • Marley Maria B. R. Vellasco
Plasticity Phenomena (Maturing, Learning & Memory)
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1606)


Genetic Algorithms (GAs) have demonstrated to be effective procedures for solving multicriterion optimization problems. These algorithms mimic models of natural evolution and have the ability to adaptively search large spaces in near-optimal ways. One direct application of this intelligent technique is in the area of evolutionary robotics, where GAs are typically used for designing behavioral controllers for robots and autonomous agents. In this paper we describe a new GA path-planning approach that proposes the evolution of a chromosome attitudes structure to control a simulated mobile robot, called Khepera. These attitudes define the basic robot actions to reach a goal location, performing straight motion and avoiding obstacles. The GA fitness function, employed to teach robot’s movements, was engineered to achieve this type of behavior in spite of any changes in Khepera’s goals and environment. The results obtained demonstrate the controller’s adaptability, displaying near-optimal paths in different configurations of the environment.

Index Terms

Genetic Algorithm robot path planning chromosome 


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  1. 1.
    Balakrishnan, K. and Honavar, V.; Analysis of neurocontrollers designed by simulated evolution. Proceedings of the International Conference on Neural Networks, Washington, D.C., 1996.Google Scholar
  2. 2.
    Balakrishnan, K. and Honavar, V.; On sensor evolution in robotics. Proceedings of the First International Conference on Genetic Programming, Standford University, CA, pp. 455–460, 1996.Google Scholar
  3. 3.
    Balakrishnan, K. and Honavar, V.; Some Experiments in Evolutionary Synthesis of Robotic Neurocontrollers. Proceedings of the World Congress on Neural Networks (WCNN’96), San Diego, CA, September 15–20, pp. 1035–1040, 1996.Google Scholar
  4. 4.
    Baluja, S.; Evolution on artificial neural networks based autonomous land vehicle controller, IEEE Transactions on Systems, Man, and Cybernetics, vol. 26, no. 3, pp. 450–463, June 1996.CrossRefGoogle Scholar
  5. 5.
    Brooks, R. A.; Artificial life and real robots. Towards a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, edited by F. J. Varela, P. Bourgine. Cambridge, MA: MIT Press/Bradford Books, 1992.Google Scholar
  6. 6.
    Davis, L.; Handbook of Genetic Algorithms, VNR Comp. Library, 1990.Google Scholar
  7. 7.
    Dorigo, M. and Schnepf, U.; Genetic-based machine learning and behavior based robotics: A new synthesis, IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 1, pp. 141–154, 1993.CrossRefGoogle Scholar
  8. 8.
    Floreano, D. and Mondada, F.; Evolution of Homing Navigation in a Real Mobile Robot, IEEE Transactions on Systems, Man, and Cybernetics, vol. 26, no. 3, pp. 396–407, June 1996.CrossRefGoogle Scholar
  9. 9.
    Goldberg, D.; Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989.Google Scholar
  10. 10.
    Hoffmann, F.; Evolutionary Learning of Mobile Robot Behaviors, the First Workshop on Frontiers in Evolutionary Algorithms FEA’97. Downloadable from the World Wide Web at http://HTTP.cs.Berkeley.EDU/~fhoffmanGoogle Scholar
  11. 11.
    Koza, J. R.; Genetic Programming. A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems. Technical Report STAN-CS-90-1314. Stanford University Computer Science Department, 1990.Google Scholar
  12. 12.
    Michel, O.; Khepera Simulator Package, version 2.0: Freeware mobile robot simulator written at the University of Nice Sophia-Antipolis by Olivier Michel. Downloadable from the World Wide Web at Scholar
  13. 13.
    Nolfi, S. et al.; How to evolve autonomous robots: different approaches in evolutionary robotics, Proceedings of the Fourth WorkShop on Artificial Life, 1994.Google Scholar
  14. 14.
    Xiao, J. and Zhang, L.: Adaptive Evolutionary Planner/Navigator for Mobile Robots, IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 18–28, April 1997.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Carlos E. Thomaz
    • 1
  • Marco Aurélio C. Pacheco
    • 1
    • 2
  • Marley Maria B. R. Vellasco
    • 1
    • 2
  1. 1.Departamento de Engenharia ElétricaPontifícia Universidade Católica-PUC/RioBrazil
  2. 2.Departamento de Engenharia de Sistemas e ComputaçãoUniversidade do Estado do Rio de Janeiro-UERJBrazil

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