An Autonomous Mobile Robot Control Method based on Fuzzy-Artificial Immune Networks and RBFN

퍼지-인공면역망과 RBFN에 의한 자율이동로봇 제어

  • 오홍민 (현대기아자동차㈜연구소) ;
  • 박진현 (진주산업대 메카트로닉스공학과) ;
  • 최영규 (부산대 전자전기정보컴퓨터공학부)
  • Published : 2003.12.01

Abstract

In order to navigate the mobile robots safely in unknown environments, many researches have been studied to devise navigational algorithms for the mobile robots. In this paper, we propose a navigational algorithm that consists of an obstacle-avoidance behavior module, a goal-approach behavior module and a radial basis function network(RBFN) supervisor. In the obstacle-avoidance behavior module and goal-approach behavior module, the fuzzy-artificial immune networks are used to select a proper steering angle which makes the autonomous mobile robot(AMR) avoid obstacles and approach the given goal. The RBFN supervisor is employed to combine the obstacle-avoidance behavior and goal-approach behavior for reliable and smooth motion. The outputs of the RBFN are proper combinational weights for the behavior modules and velocity to steer the AMR appropriately. Some simulations and experiments have been conducted to confirm the validity of the proposed navigational algorithm.

Keywords

References

  1. O. Khatib, 'Real-time obstacle avoidance for manipulators and mobile robots,' Proc. of the IEEE International Conference on Robotics and Automation, pp. 500-505, 1985 https://doi.org/10.1109/ROBOT.1985.1087247
  2. J. Borenstein and Y. Koren, 'Potential field methods and their inherent limitations for mobile robot navigation,' Proc. of the IEEE International Conference on Robotics and Automation, pp. 1398-1404, 1991 https://doi.org/10.1109/ROBOT.1991.131810
  3. J. Borenstein and Y. Koren, 'The vector field histogram-fast obstacle avoidance for mobile robots,' IEEE Transactions on Robotics and Automation, vol. 7, pp. 278-288, 1991 https://doi.org/10.1109/70.88137
  4. H. R. Beam and H. S. Cho, 'A sensor-based obstacle avoidance controller for a mobile robot using fuzzy logic and neural network,' Proc. of the IEEE International Conference on Intelligent Robots and Systems, vol. 2, pp. 1470-1475, 1992 https://doi.org/10.1109/IROS.1992.594576
  5. W. Li, 'Fuzzy logic based robot navigation in uncertain environments by multi sensor integration,' Proc. of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 259-264, 1994 https://doi.org/10.1109/MFI.1994.398444
  6. K. Pal Prabir and Kar Asim, 'Mobile robot navigation using a neural net,' Proc. of the IEEE International Conference on Robotics and Automation, vol. 2, pp. 1503 -1508, 1995 https://doi.org/10.1109/ROBOT.1995.525488
  7. A. Ishiguro, R. Watanabe, and Y. Uchikawa, 'Immunoid: an architecture for behavior arbitration based on the immune networks,' Proc. of the IEEE International Conference on Intelligent Robots and Systems, vol. 3, pp. 1730-1738, 1996 https://doi.org/10.1109/IROS.1996.569044
  8. A. Ishiguro, R. Watanabe, and Y. Uchikawa, 'Emergent construction of behavior arbitration mechanism based on the immune system,' Proc. of the IEEE International Conference on Evolutionary Computation, pp. 481-486, 1998 https://doi.org/10.1109/ICEC.1998.699855
  9. D. Dasqupta, Artificial Immune Systems and Their Applications, Springer- Verlag Berlin Heidelberg, Germany, 1999
  10. 김양현, 이동제, 이민중, 최영규, '인공면역망과 퍼지 시스템을 이용한 자율이동로봇 주행,' 대한전기학회 논문지, 제 51D권 8호, pp. 402-412, 2002 https://doi.org/10.1109/IROS.1996.569044
  11. W. L. Xu and S. K. Tso, 'Sensor-based fuzzy reactive navigation of a mobile robot through local target switching,' IEEE Transactions on Systems, Man, and Cybernetics, vol. 29, no. 3, pp.451-459, 1999 https://doi.org/10.1109/5326.777079
  12. F. Michaud, G. Lachiver, and Chon Tam Le Dinh, 'Fuzzy selection and blending of behaviors for situated autonomous agent,' Proc. of the IEEE International Conference on Fuzzy Systems, vol. 1, pp. 258-264, 1996 https://doi.org/10.1109/FUZZY.1996.551751
  13. J. Yen and N. Pfluger, 'A fuzzy logic based extension to Payton and Rosenblatt's command fusion method for mobile robot navigation,' IEEE Transactions on Systems, Man and Cybernetics, vol. 25, pp, 971-978, 1995 https://doi.org/10.1109/21.384260
  14. J. S. R. Jang, C. T. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice-Hall, 1997
  15. Proc. of the IEEE International Conference on Fuzzy Systems v.1 Fuzzy selection and blending of behaviors for situated autonomous agent F.Michaud;G.Lachiver;Chon Tam Le Dinh
  16. IEEE Transactions on Systems, Man and Cybernetics v.25 A fuzzy logic based extension to Payton and Rosenblatt's command fusion method for mobile robot navigation J.Yen;N.Pfluger https://doi.org/10.1109/21.384260
  17. Neuro-Fuzzy and Soft Computing J.S.R.Jang;C.T.Sun;E.Mizutani