Skip to main content
SpringerLink
Log in

Reactive Immune Network Based Mobile Robot Navigation

  • Conference paper
Artificial Immune Systems (ICARIS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3239))

Included in the following conference series:

Abstract

In this paper, a Reactive Immune Network (RIN) is proposed and applied to intelligent mobile robot learning navigation strategies within unknown environments. Rather than building a detailed mathematical model of immune systems, we try to explore the principle in immune network focusing on its self-organization, adaptive learning capability and immune memory. Modified virtual target method is integrated to solve local minima problem. Several trap situations designed by early researchers are employed to evaluate the performance of the proposed immunized architecture. Simulation results show that the robot is capable to avoid obstacles, escape traps, and reach goal effectively.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baraquand, J., Latombe, J.C.: Monte-Carlo algorithm for path planning with many degrees of freedom. In: Proceedings of the IEEE International Conference on Robotics and Automation, Cincinnati, OH, pp. 1712–1717 (1990)

    Google Scholar 

  2. Chang, H.: A new technique to handle local minima for imperfect potential field based motion planning. In: Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, MN, pp. 108–112 (1996)

    Google Scholar 

  3. Lee, S., Adams, T.M., Ryoo, B.-Y.: A fuzzy navigation system for mobile construction robots. Automation in Construction 6, 97–107 (1997)

    Article  Google Scholar 

  4. Borenstein, J., Koren, Y.: Real-time obstacle avoidance for fast mobile robots. IEEE Transaction on System, Man and Cybernetics 9(5), 1179–1197 (1989)

    Article  Google Scholar 

  5. Park, M.G., Lee, M.C.: Artificial potential field based path planning for mobile robots using a virtual obstacle concept. In: Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 735–740 (2003)

    Google Scholar 

  6. Xu, W.L.: A virtual target approach for resolving the limit cycle problem in navigation of a fuzzy behaviour-based mobile robot. Robotics and Autonomous Systems 30, 315–324 (2000)

    Article  Google Scholar 

  7. Chatterjee, R., Matsuno, F.: Use of single side refles for autonomous navigation of mobile robots in unknown environments. Robotics and Autonomous Systems 35, 77–96 (2001)

    Article  MATH  Google Scholar 

  8. Lee, D.-J., Lee, M.-J., Choi, Y.-K.: S., Kim, Design of autonomous mobile robot action selector based on a learning artificial immune network structure. In: Proceedings of the fifth Symposium on Artificial Life and Robotics, Oita, Japan, pp. 116–119 (2000)

    Google Scholar 

  9. Ishiguro, A., Kondo, T., Watanabe, Y., Shirai, Y., Uchikawa, Y.: Emergent construction of artificial immune networks for autonomous mobile robots. In: IEEE International Conference on Systems, Man, and Cybernetics, Orlando, Florida, pp. 1222–1228 (1997)

    Google Scholar 

  10. KrishnaKumar, K., Neidhoefer, J.: Immunized Neuro control. Expert Systems With Applications 13(3), 201–214 (1997)

    Article  Google Scholar 

  11. Luh, G.-C., Cheng, W.-C.: Behavior-based intelligent mobile robot using immunized reinforcement adaptive learning mechanism. Advanced Engineering Informatics 16(2), 85–98 (2002)

    Article  Google Scholar 

  12. Michelan, R., Fernando, J.V.: Decentralized control system for autonomous navigation based on an evolved artificial immune network. In: Proceedings, pp. 1021-1026 (2002)

    Google Scholar 

  13. Dasgupta, D.: Artificial Immune Systems and Their Applications. Springer, Heidelberg (1999)

    MATH  Google Scholar 

  14. de Castro, L.N., Jonathan, T.: Artificial immune systems: A new Computational Intelligence Approach. Springer, Heidelberg (1999)

    Google Scholar 

  15. Y., The immune system as a prototype of autonomous decentralized systems: an overview. In: Proceedings of Third International Symposium on autonomous decentralized systems, pp. 85-92 (1997)

    Google Scholar 

  16. Roitt, I., Brostoff, J., Male, D.K.: Immunology, 5th edn. Mosby International Limited (1998)

    Google Scholar 

  17. Oprea, M.L.: Antibody repertories and pathogen recognition: the role of germline diversity and somatic hypermutation, PhD Dissertation, Department of Computer Science, The University of New Mexico, Albuquerque, New Mexico (1996)

    Google Scholar 

  18. Carneiro, J., Coutinho, A., Faro, J., Stewart, J.: A model of the immune network with B-T cell co-operation I-prototypical structures and dynamics. Journal of theoretical Biological 182, 513–529 (1996)

    Article  Google Scholar 

  19. Dasgupta, D.: Artificial neural networks and artificial immune systems: similarities and differences. In: IEEE International Conference on Systems, Man, and Cybernetics, Orlando, Florida, pp. 873–878 (1997)

    Google Scholar 

  20. Jerne, N.K.: The immune system. Scientific American 229(1), 52–60 (1973)

    Article  Google Scholar 

  21. Arkin, R.C.: Behavior-based robotics. MIT Press, Cambridge (1998)

    Google Scholar 

  22. Farmer, J.D., Packard, N.H., Perelson, A.S.: The immune system adaptation, and machine learning. Physica 22-D, 184- 204 (1986)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Tatung University, 40 Chungshan North Road, 3rd sec, Taipei, Taiwan, R.O.C.

    Guan-Chun Luh & Wei-Wen Liu

Authors
  1. Guan-Chun Luh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei-Wen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125, Catania, Italy

    Giuseppe Nicosia  & Vincenzo Cutello  & 

  2. Computer Science Department, University College London, London, UK

    Peter J. Bentley

  3. Department of Electronics, University of York, YO10 5DD, Heslington, York, UK

    Jon Timmis

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Luh, GC., Liu, WW. (2004). Reactive Immune Network Based Mobile Robot Navigation. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds) Artificial Immune Systems. ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30220-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30220-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23097-7

  • Online ISBN: 978-3-540-30220-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation