Automatic Car Parking: A Reinforcement Learning Approach

  • Darío Maravall
  • Miguel Ángel Patricio
  • Javier de Lope
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2686)


The automatic parking of a car-like robot is the problem considered in this paper to evaluate the role played by formal representations and models in neural-based controllers. First, a model-free control scheme is introduced. The respective control actions are sensory-based and consist of a dynamic, neural-based process in which the neurocontroller optimizes ad hoc performance functions. Afterwards, a model-based neurocontroller that builds without supervised a formal representation of its interaction with the environment is proposed. The resulting model is eventually utilized to generate the control actions. Simulated experimentation has shown that there is an improvement in robot behavior when a model is used, at the cost of higher complexity and computational load.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Laumond, J.-P., Jacobs, P.E., Taix, M., Murray, R.M. (1996) A motion planner for non-holonomic mobile robots. IEEE Trans. On Robotics and Automation, 10(5), 577–593.CrossRefGoogle Scholar
  2. 2.
    Paromtchik, I.E., Laugier, C. (1996) Motion generation and control for parking an autonomous vehicle. Proc. IEEE Int. Conference on Robotics and Automation, Minneapolis, MN, 3117–3122.Google Scholar
  3. 3.
    Laugier, C., Fraichard, Th., Garnier, Ph., Paromtchik, I.E., Scheuer, A. (1999) Sensor-based control architecture for a car-like vehicle. Autonomous Robots 6, 165–185.CrossRefGoogle Scholar
  4. 4.
    Kong, S.G., Kosko, B. (1992) “Comparison of fuzzy and neural track backer-upper control systems”. In B. Kosko (ed). Neural Networks and Fuzzy Systems. Prentice-Hall. Englewood Cliffs, NJ, 339–361.Google Scholar
  5. 5.
    Gu, D., Hu, H. (2002) Neural predictive control for a car-like mobile robot. Robotics and Autonomous Systems 39, 73–86.CrossRefGoogle Scholar
  6. 6.
    Hitchings, M., Vlacic, L., Kecman, V. (2001) “Fuzzy control”. In L. Vlacic, M. Parent, F. Harashima (eds). Intelligent Vehicle Technologies. Butterworth & Heinemann, Oxford, 289–331.CrossRefGoogle Scholar
  7. 7.
    Canudas de Wit, C. (1998) “Trends in mobile robots and vehicle control”. In B. Siciliano, K. P. Valavanis (eds). Control Problems in Robotics and Automation. LNCIS 230, Springer, Berlin, 151–176.Google Scholar
  8. 8.
    Maravall, D., de Lope, J. (2002) “A reinforcement learning method for dynamic obstacle avoidance in robotic mechanisms”. In D. Ruan, P. D’hondt, E. E. Kerre (eds). Computational Intelligent Systems. World Scientific, Singapore, 485–494.Google Scholar
  9. 9.
    Maravall, D., de Lope, J. (2003) “A bio-inspired robotic mechanism for autonomous locomotion in unconventional environments”. In C. Zhou, D. Maravall, D. Ruan (eds). Autonomous Robotic Systems: Soft Computing and Hard Computing Methodologies and Applications. Physica-Verlag, Springer, Heidelberg, 263–292.Google Scholar
  10. 10.
    Zhou, C., Meng, Q. (2003) Dynamic balance of a biped robot using fuzzy reinforcement learning agents. Fuzzy Sets and Systems, 134(1), 169–187.zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Darío Maravall
    • 1
  • Miguel Ángel Patricio
    • 2
  • Javier de Lope
    • 1
  1. 1.Department of Artificial Intelligence Faculty of Computer ScienceUniversidad Politécnica de MadridMadridSpain
  2. 2.Departamento de InformáticaUniversidad Carlos III de MadridMadrid

Personalised recommendations