Endocrine Control for Task Distribution among Heterogeneous Robots

  • Joanne H. Walker
  • Myra S. Wilson
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 83)


This paper details an endocrine based system which automatically reassigns tasks among heterogeneous robots dependent on the ability of the robot to do the task. This ability (or sensitivity) to a task is initialised for each individual robot after an evolutionary training stage, then constantly adapts as the robots perform the various tasks. The system does not require a centralised controller, and relies on little communication between the robots.


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  1. 1.
    Arkin, R.: Survivable Robotic Systems: Reactive and Homeostatic Control. Prentice-Hall (1993)Google Scholar
  2. 2.
    Arkin, R.C.: Motor Schema–Based Mobile Robot Navigation. International Journal of Robotics Research 8(4), 9–12 (1989)Google Scholar
  3. 3.
    Brooks, R.: Integrated systems based on behaviours. Sigart Bulletin 2, 46–50 (1991)CrossRefGoogle Scholar
  4. 4.
    Maes, P.: The Dynamics of Action Selection. In: 11th International Joint Conference on Artificial Intelligence, vol. 2, pp. 991–997 (1989)Google Scholar
  5. 5.
    Mendao, M.: Neuro-Endocrine Control Architectures applied to Mobile Robotics. Ph.D Thesis, University of Kent, Canterbury, UK (2008)Google Scholar
  6. 6.
    Neal, M., Timmis, J.: Timidity: A Useful Emotional Mechanism for Robot Control? Informatica 27, 197–203 (2003)MATHGoogle Scholar
  7. 7.
    Parker, L.E.: ALLIANCE: An Architecture for Fault Tolerant Multi-Robot Cooperation. IEEE Transactions on Robotics and Automation 14(2), 220–240 (1998)CrossRefGoogle Scholar
  8. 8.
    Ram, A., Arkin, R., Boone, G., Pearce, M.: Using Genetic Algorithms to Learn Reactive Control Parameters for Autonomous Robotic Navigation. Adaptive Behavior 2(3), 277–304 (1994)CrossRefGoogle Scholar
  9. 9.
    Shen, W.M., Salemi, B., Will, P.: Hormone-Inspired Adaptive Communication and Distributed Control for CONRO Self-Reconfigurable Robots. IEEE Transactions on Robotics and Automation 18(5) (2002)Google Scholar
  10. 10.
    Walker, J.: Experiments in Evolutionary Robotics: Investigating the Importance of Training and Lifelong Adaptation by Evolution. Ph.D. Thesis, Department of Computer Science, University of Wales, Aberystwyth, UK (2003)Google Scholar
  11. 11.
    Walker, J., Garrett, S., Wilson, M.: The balance between initial training and lifelong adaptation in evolving robot controllers. IEEE Transactions on Systems, Man and Cybernetics: Part B 36, 423–432 (2006)CrossRefGoogle Scholar
  12. 12.
    Walker, J., Wilson, M.S.: Lifelong Evolution for Adaptive Robots. In: International Conference on Intelligent Robots and Systems (IROS), pp. 984–989 (2002)Google Scholar
  13. 13.
    Walker, J., Wilson, M.S.: Hormone-Inspired Control for Group Task-Distribution. In: Towards Autonomous Robotic Systems, TAROS (2007)Google Scholar
  14. 14.
    Walker, J., Wilson, M.S.: A Performance Sensitive Hormone-Inspired System for Task Distribution Amongst Evolving Robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (2008)Google Scholar
  15. 15.
    Watson, R., Ficici, S., Pollack, J.: Embodied Evolution: Embodying an Evolutionary Algorithm in a Population of Robots. In: Congress on Evolutionary Computation (CEC), pp. 335–342 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceAberystwyth UniversityPenglaisUK

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