A CONTROL SYSTEM USING BEHAVIOUR HIERARCHIES AND NEURO-FUZZY APPROACH

Dilek Arslan, Ferda N Alpaslan

2005

Abstract

In agent-based systems, especially in autonomous mobile robots, modelling the environment and its changes is a source of problems. It is not always possible to effectively model the uncertainty and the dynamic changes in complex, real-world domains. Control systems must be robust to changes and must be able to handle the uncertainties to overcome this problem. In this study, a reactive behaviour based agent control system is modelled and implemented. The control system is tested in a navigation task using an environment, which has randomly placed obstacles and a goal position to simulate an environment similar to an autonomous robot’s indoor environment. Then the control system was extended to control an agent in a multi-agent environment. The main motivation of this study is to design a control system, which is robust to errors and is easy to modify. Behaviour based approach with the advantages of fuzzy reasoning systems is used in the system.

References

  1. Ahrns, I., J. Bruske, G. Hailu, and G. Sommer, 1998. Neural fuzzy techniques in sonarbased collision avoidance. Soft Computing for Intelligent Robotic Systems, pages 185-214. Physica.
  2. Bonarini, A., 1996. Evolutionary learning of fuzzy rules: competition and cooperation. Fuzzy Modeling: Paradigms and Practice, pages 265-284. Kluwer Academic Press, Norwell, MA.
  3. Brooks, R. A., 1986. A Robust Layered Control System for a Mobile Robot. IEE Journal of Robotics and Automation, Vol. RA-2, No.1, pp 14-23.
  4. Godjavec, J., N. Steele, 2000. Neuro-fuzzy control for basic mobile robot behaviors. In Fuzzy Logic Techniques for Autonomous Vehicle Navigation, pages 97-117.
  5. Hagras, H.,V. Callaghan, and M.Colley, 2000. Learning fuzzy behavior co-ordination for autonomous multiagents online using genetic algorithms and real-time interaction with the environment. Fuzzy IEEE.
  6. Hagras, H., V. Callaghan, 2001. A Hierarchical FuzzyGenetic Multi-Agent Architecture for Intelligent Buildings Online Learning, Adaptation and Control. International Journal of Information Sciences.
  7. Jang, Jyh-Shing R., 1993. ANFIS: Adaptive-NetworkBased Fuzzy Inference System. IEEE Trans. Systems, Man & Cybernetics, Vol. 23, pp 665-685.
  8. Lin, Y., G. Cunningham, 1995. A New Approach to Fuzzy-Neural System Modeling. IEEE Transactions On Fuzzy Systems. Vol.3, No.2.
  9. Saffiotti, A., 1997. The Use of Fuzzy Logic for Autonomous Robot Navigation. Soft Computing, Vol. 1(4), pp 180-197.
  10. Tunstel, E., M. Jamshidi, 1996. On Genetic Programming of Fuzzy Rule-Based Systems for Intelligent Control. International Journal of Intelligent Automation & Soft Computing, Vol.2 No.3, pp. 273-284.
  11. Tunstel, E., T. Lippincott, M. Jamshidi, 1997. Behaviour Hierarchy for Autonomous Mobile Robots: FuzzyBehaviour Modulation and Evolution. International Journal of Intelligent Automation & Soft Computing, Vol.3, No.1, Special Issue on Autonomous Control Engineering, pp. 37-50.
  12. Tunstel, E., M. Oliveira, S. Berman, 2002. Fuzzy Behaviour Hierarchies for Multi-Robot Control. International Journal of Intelligent Systems, Vol.17 449-470..
  13. Zadeh, L. A., 1965. Fuzzy Sets. Information and Control, No. 8, pp. 338-353.
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Paper Citation


in Harvard Style

Arslan D. and N Alpaslan F. (2005). A CONTROL SYSTEM USING BEHAVIOUR HIERARCHIES AND NEURO-FUZZY APPROACH . In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 972-8865-29-5, pages 222-229. DOI: 10.5220/0001158802220229


in Bibtex Style

@conference{icinco05,
author={Dilek Arslan and Ferda N Alpaslan},
title={A CONTROL SYSTEM USING BEHAVIOUR HIERARCHIES AND NEURO-FUZZY APPROACH},
booktitle={Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2005},
pages={222-229},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001158802220229},
isbn={972-8865-29-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - A CONTROL SYSTEM USING BEHAVIOUR HIERARCHIES AND NEURO-FUZZY APPROACH
SN - 972-8865-29-5
AU - Arslan D.
AU - N Alpaslan F.
PY - 2005
SP - 222
EP - 229
DO - 10.5220/0001158802220229