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The Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms

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Artifical Evolution (EA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5975))

Abstract

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 (a virtual Pioneer P3-DX) that also differs in shape, sensor type, sensor configuration and programming interface. The chosen architecture uses a reinforcement learning-assisted genetic algorithm to evolve the epuck behaviours, which are encoded as a genetic sequence. This sequence is then used by the Pioneers as part of an adaptive, idiotypic artificial immune system (AIS) control architecture. Testing in three different simulated worlds shows that the Pioneer can use these behaviours to navigate and solve object-tracking tasks successfully, as long as its adaptive AIS mechanism is in place.

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References

  1. Farmer, J.D., Packard, N.H., Perelson, A.S.: The Immune System, Adaptation, and Machine Learning. Physica D 2(1-3), 187–204 (1986)

    Article  MathSciNet  Google Scholar 

  2. Floreano, D., Mondada, F.: Evolutionary Neurocontrollers for Autonomous Mobile Robots. Neural Networks 11(7-8), 1416–1478 (1998)

    Article  Google Scholar 

  3. Floreano, D., Mondada, F.: Hardware Solutions for Evolutionary Robotics. In: Proceedings of the First European Workshop on Evolutionary Robotics, pp. 137–151. Springer, London (1998)

    Google Scholar 

  4. Floreano, D., Urzelai, J.: Evolutionary Robots with On-line Self-organization and Behavioural Fitness. Neural Networks 13, 431–443 (2000)

    Article  Google Scholar 

  5. Gerkey, B., Vaughan, R., Howard, A.: 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 (2003)

    Google Scholar 

  6. Goosen, T., van den Brule, R., Janssen, J., Haselager, P.: Interleaving Simulated and Physical Environments Improves Evolution of Robot Control Structures. In: Proceedings of the 19th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC), pp. 135–142. Utrecht University Press (2007)

    Google Scholar 

  7. Jerne, N.K.: Towards a Network Theory of the Immune System. Annales d’Immunologie 125C(1-2), 373–389 (1974)

    Google Scholar 

  8. Krautmacher, M., Dilger, W.: AIS Based Robot Navigation in a Rescue Scenario. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 106–118. Springer, Heidelberg (2004)

    Google Scholar 

  9. Lungarella, M., Sporns, O.: Mapping Information Flow in Sensorimotor Networks. PLOS Computational Biology 2(1), 1301–1312 (2006)

    Article  Google Scholar 

  10. Michel, O.: Cyberbotics Ltd - WebotsTM: Professional Mobile Robot Simulation. International Journal of Advanced Robotic Systems 1(1), 39–42 (2004)

    Google Scholar 

  11. Nolfi, S., Floreano, D.: Evolutionary Robotics, The Biology, Intelligence, and Technology of Self-Organizing Machines, 1st edn. MIT Press, Cambridge (2000)

    Google Scholar 

  12. Urzelai, J., Floreano, D.: Evolutionary Robotics: Coping with Environmental Change. In: Proceedings of the Genetic and Evolutionary Computation Conference GECCO-00, pp. 941–948. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  13. Utz, H., Sablatnog, S., Enderle, S., Kraetzschmar, G.: Miro- Middleware for Mobile Robot Applications. IEEE Transactions on Robotics and Automation 18(4), 493–497 (2002)

    Article  Google Scholar 

  14. Walker, J.H., Garett, S.M., Wilson, M.S.: The Balance Between Initial Training and Lifelong Adaptation in Evolving Robot Controllers. IEEE Transactions on Systems, Man and Cybernetics- Part B: Cybernetics 36(2), 423–432 (2006)

    Article  Google Scholar 

  15. Whitbrook, A.M., Aickelin, U., Garibaldi, J.M.: Idiotypic Immune Networks in Mobile Robot Control. IEEE Transactions on Systems, Man and Cybernetics, Part B- Cybernetics 37(6), 1581–1598 (2007)

    Article  Google Scholar 

  16. Whitbrook, A.M., Aickelin, U., Garibaldi, J.M.: An Idiotypic Immune Network as a Short-Term Learning Architecture for Mobile Robots. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 266–278. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  17. Whitbrook, A.M., Aickelin, U., Garibaldi, J.M.: 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 (2008)

    Google Scholar 

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Whitbrook, A.M., Aickelin, U., Garibaldi, J.M. (2010). The Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms. In: Collet, P., Monmarché, N., Legrand, P., Schoenauer, M., Lutton, E. (eds) Artifical Evolution. EA 2009. Lecture Notes in Computer Science, vol 5975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14156-0_11

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  • DOI: https://doi.org/10.1007/978-3-642-14156-0_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14155-3

  • Online ISBN: 978-3-642-14156-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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