Advertisement

Don’t Touch Me, I’m Fine: Robot Autonomy Using an Artificial Innate Immune System

  • Mark Neal
  • Jan Feyereisl
  • Rosario Rascunà
  • Xiaolei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4163)

Abstract

A model for integration of low-level responses to damage, potential damage and component failure in robots is presented. This model draws on the notion of inflammation and introduces an extensible, sub-symbolic mechanism for modulating high-level behaviour using the notion of artificial inflammation. Preliminary results obtained via simulation are presented and demonstrate the potential benefits of such a scheme. Additionally the system maps the robot’s physiological state-space, which is defined in terms of the levels and sources of inflammatory response. This is achieved using Kohonen’s Self-Organizing Map algorithm to arrange the states experienced during the lifetime of the robot. The future use of this map for diagnosis and localization of faults and for the generation of specific high-level remediation behaviour is also discussed.

Keywords

Artificial Immune Systems Human Immune Systems Innate Immunity TLR PAMPs Inflammation SOM Robot 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brooks, R.: A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation 2(1), 14–23 (1986)CrossRefGoogle Scholar
  2. 2.
    Clancey, W.: Situated Cognition: On Human Knowledge and Computer Representations. Cambridge University Press, Cambridge (1997)Google Scholar
  3. 3.
    de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  4. 4.
    Greensmith, J., Aickelin, U., Cayzer, S.: Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 153–167. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Kohonen, T.: Self-organised formation of topologically correct feature maps. Biological Cybernetics 43, 59–69Google Scholar
  6. 6.
    Kohonen, T.: Self-organising Maps. Springer, Heidelberg (1995)Google Scholar
  7. 7.
    Neal, M., Timmis, J.: Timidity: A Useful Mechanism for Robot Control? Informatica 27(4), 197–204 (2003)zbMATHGoogle Scholar
  8. 8.
    Neal, M., Timmis, J.: Once more unto the breach.. towards artificial homeostasis? In: Recent Advances in Biologically Inspired Computing, IGP (2004)Google Scholar
  9. 9.
    Sompayrac, L.: How the immune system works. Blackwell, Malden (2002)Google Scholar
  10. 10.
    Trapnell Jr., B.C.: A peer-to-peer blacklisting strategy inspired by leukocyte-endothelium interaction. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 339–352. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mark Neal
    • 1
  • Jan Feyereisl
    • 2
  • Rosario Rascunà
    • 3
  • Xiaolei Wang
    • 4
  1. 1.Computer ScienceUniversity of WalesAberystwythUK
  2. 2.School of Computer ScienceUniversity of NottinghamUK
  3. 3.CCNRUniversity of SussexUK
  4. 4.Electrical and Communications EngineeringHelsinki University of TechnologyEspooFinland

Personalised recommendations