Artificial Immune System Based Path Planning of Mobile Robot

  • P. K. Das
  • S. K. Pradhan
  • S. N. Patro
  • B. K. Balabantaray
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 395)

Abstract

Planning of the optimal path has always been the target pursued by many researchers since last five decade. Its application on mobile robot is one of the most important research topics among the scientist and researcher. This paper aims to plan the obstacle-avoiding path for mobile robots based on the Artificial Immune Algorithm (AIA) developed from the immune principle. An immunity algorithm adapting capabilities of the immune system is proposed and enable robot to reach the target object safely and successfully fulfill its task through optimal path and with minimal rotation angle efficiency. Finally, we have compared with the GA based path planning with the AIA based path planning. Simulation results show that the mobile robot is capable of avoiding obstacles, escaping traps, and reaching the goal efficiently and effectively by using AIA than GA.

Keywords

Path planning Artificial immune algorithm Robotics Navigation GA 

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References

  1. 1.
    Kcymeulcn, D., Decuyper, J.: The Fluid Dynamics applied to Mobile Robot Motion: the Stream Field Method [A]. In: 1994 IEEE Intemational Conference on Robotics and Automation, pp. 378–385. Sponsored by IEEE Robotics and Automation Society, San Diego (1994)Google Scholar
  2. 2.
    Chen, G., Shen, L.: Genetic path planning algorithm under complex environment. Robot 23(1), 40–43 (2001)Google Scholar
  3. 3.
    Yu, J., Kromov, V., et al.: A rapid path planning algorithm of neural network. Robot 23(3), 201–205 (2001)Google Scholar
  4. 4.
    Dasgupta, D.: Artificial Immune Systems and Their Applications. Springer, Heidelberg (1999)MATHCrossRefGoogle Scholar
  5. 5.
    de Castro, L.N., Jonathan, T.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (1999)Google Scholar
  6. 6.
    de Castro, L.N., Von Zuben, F.J.: Artificial immune systems. Part I. Basic theory and applications. Technical Report TR-DCA 01/99 (1999)Google Scholar
  7. 7.
    de Castro, L.N., Von Zuben, F.J.: Artificial immune systems. Part II. A survey of applications. Technical Report TR-DCA 02/00 (1999)Google Scholar
  8. 8.
    Cen, L., Bodkin, B., Lancaster, J.: Programming Khepera II Robot for Autonomous Navigation and Exploration using the Hybrid Architecture. In: ACMSE 2009, Clemson, Sc, USA, March 19-21 (2009)Google Scholar
  9. 9.
    Luh, G.-C., Cheng, W.-C.: Behavior-based intelligent mobile robot using immunized reinforcement adaptive learning mechanism. Adv. Eng. Informat. 16(2), 85–98 (2002)CrossRefGoogle Scholar
  10. 10.
    Lee, D.-J., Lee, M.-J., Choi, Y.-K., Kim, S.: 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
  11. 11.
    Vargas, P.A., de Castro, L.N., Michelan, R., Von Zuben, F.J.: Implementation of an Immuno-Gentic Network on a Real Khepera II Robot. In: Proceedings of the IEEE Congress on Evolutionary Computation, Canberra, Australia, pp. 420–426 (2003)Google Scholar
  12. 12.
    Duan, Q.J., Wang, R.X., Feng, H.S., Wang, L.G.: An immunity algorithm for path planning of the autonomous mobile robot. In: Proceedings of the IEEE Eighth International Multitopic Conference, Lahore, Pakistan, pp. 69–73 (2004)Google Scholar
  13. 13.
    Roitt, I., Brostoff, J., Male, D.K.: Immunology, 5th edn. Mosby International Limited (1998)Google Scholar
  14. 14.
    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 NewMexico, Albuquerque, New Mexico (1996)Google Scholar
  15. 15.
    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. J. Theor. Biol. 182, 513–529 (1996)CrossRefGoogle Scholar
  16. 16.
    Dasgupta, D.: Artificial neural networks and artificial immune systems: similarities and differences. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Orlando, Florida, pp. 873–878 (1997)Google Scholar
  17. 17.
    Konar, A.: Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain, 1st edn. CRC Press (1999)Google Scholar
  18. 18.
    Das, P.K., Konar, A., Laishram, R.: Path Planning of Mobile Robot in Unknown Environment. Special Issue of IJCCT 1(2,3,4), 26–31 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • P. K. Das
    • 1
  • S. K. Pradhan
    • 2
  • S. N. Patro
    • 1
  • B. K. Balabantaray
    • 3
  1. 1.Dhaneswar Rath Institute of Engineering and Management Studies TangiCuttackIndia
  2. 2.College of Engineering and TechnologyBhubaneswarIndia
  3. 3.Synergy Institute of TechnologyBhubaneswarIndia

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