Skip to main content

Advertisement

SpringerLink
Log in

Real-world transfer of evolved artificial immune system behaviours between small and large scale robotic platforms

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

In mobile robotics, a solid test for adaptation is the ability of a control system to function not only in a diverse number of physical environments, but also on a number of different robotic platforms. This paper demonstrates that a set of behaviours evolved in simulation on a miniature robot (epuck) can be transferred to a much larger-scale platform (Pioneer), both in simulation and in the real world. The chosen architecture uses artificial evolution of epuck behaviours to obtain a genetic sequence, which is then employed to seed an idiotypic, artificial immune system (AIS) on the Pioneers. Despite numerous hardware and software differences between the platforms, navigation and target-finding experiments show that the evolved behaviours transfer very well to the larger robot when the idiotypic AIS technique is used. In contrast, transferability is poor when reinforcement learning alone is used, which validates the adaptability of the chosen architecture.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Nolfi S, Floreano D (2000) Evolutionary robotics, the biology, intelligence, and technology of self-organizing machines. The MIT Press, Cambridge

    Google Scholar 

  2. Floreano D, Mondada F (1998) Evolutionary neurocontrollers for autonomous mobile robots. Neural Netw 11(7–8):1416–1478

    Google Scholar 

  3. Lungarella M, Sporns O (2006) Mapping information flow in sensorimotor networks. PLOS Comput Biol 2(1):1301–1312

    Article  Google Scholar 

  4. Floreano D, Mondada F (1998) Hardware solutions for evolutionary robotics. In: Proceedings of the 1st European workshop on evolutionary robotics. Springer, London, pp 137–151

  5. Walker JH, Garett SM, Wilson MS (2006) The balance between initial training and lifelong adaptation in evolving robot controllers. IEEE Trans Syst Man Cybern B Cybern 36(2):423–432

    Article  Google Scholar 

  6. Michel O (2004) Cyberbotics Ltd—WebotsTM: professional mobile robot simulation. Int J Adv Robot Syst 1(1):39–42

    Google Scholar 

  7. Hightower RR, Forrest F, Perelson AS (2005) The evolution of emergent organization in immune system gene libraries. In: Eshelman LJ (ed) Proceedings of the 6th international conference on genetic algorithms. Morgan Kaufmann, San Francisco

  8. Spellward P, Kovacs T (2005) On the contribution of gene libraries to artificial immune systems. In: Proceedings of the genetic and evolutionary computation conference, pp 313–319

  9. Goosen T, van den Brule R, Janssen J, Haselager P (2007) Interleaving simulated and physical environments improves evolution of robot control structures. In: Proceedings of the 19th Belgium–Netherlands conference on artificial intelligence (BNAIC). Utrecht University Press, Utrect, pp 135–142

  10. Whitbrook AM, Aickelin U, Garibaldi JM (2007) Idiotypic immune networks in mobile robot control. IEEE Trans Syst Man Cybern B Cybern 37(6):1581–1598

    Article  Google Scholar 

  11. Whitbrook AM, Aickelin U, Garibaldi JM (2008) An idiotypic immune network as a short-term learning architecture for mobile robots. In: Proceedings of the 7th international conference on artificial immune systems (ICARIS 2008), Phuket, Thailand. Springer, Berlin, pp 266–278

  12. Gerkey B, Vaughan R, Howard A (2003) The player/stage project: tools for multi-robot and distributed sensor systems. In: Proceedings of the international conference on advanced robotics (ICAR 2003), Coimbra, Portugal, pp 317–323

  13. Utz H, Sablatnog S, Enderle S, Kraetzschmar G (2002) Miro: Middleware for mobile robot applications. IEEE Trans Rob Autom 18(4):493–497

    Article  Google Scholar 

  14. Floreano D, Urzelai J (2000) Evolutionary robots with on-line self-organization and behavioural fitness. Neural Netw 13:431–443

    Article  Google Scholar 

  15. Urzelai J, Floreano D (2000) Evolutionary robotics: coping with environmental change. In: Proceedings of the genetic and evolutionary computation conference GECCO-00. Morgan Kaufmann, San Francisco, pp 941–948

  16. Jerne NK (1974) Towards a network theory of the immune system. Ann Immunol 125C(1–2):373–389

    Google Scholar 

  17. Aziz-Alaoui MA, Bertelle C (2006) Emergent properties in natural and artificial dynamical systems (understanding complex systems). Springer, Berlin

    Book  Google Scholar 

  18. Hoffman GW (1986) A neural network model based on the analogy with immune system. J Theor Biol 122:33–67

    Article  Google Scholar 

  19. Richter PH (1978) Complexity and regulation of the immune system: the network approach. In: Proceedings of the working conference on system theory in immunology, Rome, Italy. Springer, New York, pp 219–227

  20. Mohler RR, Bruni C, Gandolfi A (1980) A systems approach to immunology. Proc IEEE 68(8):964–990

    Article  Google Scholar 

  21. Ishida Y (1990) Fully distributed diagnosis by pdp learning algorithm: towards immune network pdp model. In: Proceedings of the international joint conference on neural networks, pp 777–782

  22. Watanabe Y, Sato S, Ishida Y (2004) An approach for self-repair in distributed system using immunity-based diagnostic mobile agents. In: Negoita MGh et al. (eds) KES 2004, LNAI 3214. Springer, Berlin, pp 504–510

    Google Scholar 

  23. Cayzer S, Aickelin U (2005) A recommender system based on idiotypic artificial immune networks. J Math Model Algorithms 4(2):181–198

    Article  MATH  Google Scholar 

  24. Suzuki J, Yamamoto Y (2000) Building an artificial immune network for decentralized policy negotiation in a communication end system: open webserver/inexus study. In: Proceedings of the 4th world conference on systemics, cybernetics and informatics (SCI 2000), Orlando

  25. Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation, and machine learning. Phys D 2(1–3):187–204

    Article  MathSciNet  Google Scholar 

  26. Krautmacher M, Dilger W (2004) AIS based robot navigation in a rescue scenario. In: Proceedings of the 3rd international conference on artificial immune systems (ICARIS 2004), Catania, Italy. Springer, Berlin, pp 106–118

  27. Kondo T, Ishiguro A, Watanabe Y, Shirai Y, Uchikawa Y (1998) Evolutionary construction of an immune network-based behavior arbitration mechanism for autonomous mobile robots. Electr Eng Jpn 123(3):1–10

    Article  Google Scholar 

  28. Michelan R, Von Zuben FJ (2002) Decentralized control system for autonomous navigation based on an evolved artificial immune network. In: Proceedings of the 2002 congress on evolutionary computation (CEC 2002), Honolulu, May 12–17 2002, vol 2, pp 1021–1026

  29. Opp WJ, Sahin F (2004) An artificial immune system approach to mobile sensor networks and mine detection. In: Proceedings of the SMC 2004, IEEE international conference on systems, man, and cybernetics, vol 1, pp 947–952

  30. Whitbrook AM, Aickelin U, Garibaldi JM (2008) Genetic algorithm seeding of idiotypic networks for mobile-robot navigation. In: Proceedings of the 5th international conference on informatics in control, automation and robotics (ICINCO 2008), Madeira, Portugal, pp 5–13

  31. Renders JM, Flasse SP (1996) Hybrid methods using genetic algorithms for global optimization. IEEE Trans Syst Man Cybern B Cybern 26(2):243–258

    Article  Google Scholar 

  32. Franz J, Ruppel CCW, Seifert F, Weigel R (1997) Hybrid optimization techniques for the design of saw-filters. In: Proceedings of the IEEE ultrasonics symposium. Springer, Berlin, pp 33–36

  33. Whitbrook AM (2010) Programming mobile robots with aria and player: a guide to object-oriented control. Springer, Berlin

    Book  Google Scholar 

Download references

Acknowledgments

This research was funded by the Engineering and Physical Sciences Research Council (EPSRC).

Author information

Authors and Affiliations

  1. Intelligent Modelling and Analysis Research Group (IMA), School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK

    Amanda M. Whitbrook, Uwe Aickelin & Jonathan M. Garibaldi

Authors
  1. Amanda M. Whitbrook
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Uwe Aickelin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jonathan M. Garibaldi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amanda M. Whitbrook.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Whitbrook, A.M., Aickelin, U. & Garibaldi, J.M. Real-world transfer of evolved artificial immune system behaviours between small and large scale robotic platforms. Evol. Intel. 3, 123–136 (2010). https://doi.org/10.1007/s12065-010-0039-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-010-0039-7

Keywords

Search

Navigation