Decentralized Multi-tasks Distribution in Heterogeneous Robot Teams by Means of Ant Colony Optimization and Learning Automata

  • Javier de Lope
  • Darío Maravall
  • Yadira Quiñonez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7208)

Abstract

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-election of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested on decentralized solution where the robots themselves autonomously and in an individual manner, are responsible of selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-tasks distribution problem and we propose a solution using two different approaches by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithm, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Keywords

Multi-robot Systems Stochastic Learning Automata Ant Colony Optimization Multi-tasks Distribution Self-Coordination of Multiple Robots Reinforcement Learning Multi-Heterogeneous Specialized Tasks Distribution 

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References

  1. 1.
    Gerkey, B., Mataric, M.: Multi-Robot Task Allocation: Analyzing the Complexity and Optimality of Key Architectures. In: IEEE International Conference on Robotics and Automation, pp. 3862–3868 (2003)Google Scholar
  2. 2.
    Gerkey, B., Mataric, M.: A formal analysis and taxonomy of task allocation in multi-robot systems. Intl. J. of Robotics Research, 939–954 (2004)Google Scholar
  3. 3.
    Farinelli, A., Locchi, L., Nardi, D.: Multirobot systems: a classification focused on coordination. IEEE Transactions on Systems, Man and Cybernetics, 2015–2028 (2004)Google Scholar
  4. 4.
    Oster, G., Wilson, E.: Caste and ecology in the social insects. Monographs in Population Biology. Princeton Univ. Press (1978)Google Scholar
  5. 5.
    Robinson, G.: Regulation of division of labor in insect societies. Annu. Rev. Entomol., 637–665 (1992)Google Scholar
  6. 6.
    Quiñonez, Y., de Lope, J., Maravall, D.: Bio-inspired Decentralized Self-coordination Algorithms for Multi-heterogeneous Specialized Tasks Distribution in Multi-Robot Systems. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2011, Part I. LNCS, vol. 6686, pp. 30–39. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Quiñonez, Y., Maravall, D., de Lope, J.: Stochastic Learning Automata for Self-coordination in Heterogeneous Multi-Tasks Selection in Multi-Robot Systems. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part I. LNCS, vol. 7094, pp. 443–453. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Gerkey, B., Mataric, M.: Multi-robot task allocation: analyzing the complexity and optimality of key architectures. In: IEEE International Conference on Robotics and Automation, pp. 3862–3868 (2003)Google Scholar
  9. 9.
    Narendra, K., Thathachar, M.: Learning Automata: An Introduction. Prentice-Hall, Englewood Cliffs (1989)Google Scholar
  10. 10.
    Narendra, K., Thathachar, M.: Learning Automata: A Survey. IEEE Transactions on Systems, Man, and Cybernetics, 323–334 (1974)Google Scholar
  11. 11.
    Obaidat, M., Papadimitriou, G., Pomportsis, A.: Guest Editorial Learning Automata: Theory, Paradigms, and Applications. IEEE Transactions on Systems, Man, and Cybernetics–Part B: Cybernetics, 706–709 (2002)Google Scholar
  12. 12.
    Maravall, D., De Lope, J.: Fusion of Learning Automata Theory and Granular Inference Systems: ANLAGIS. Applications to Pattern Recognition and Machine Learning. Neurocomputing 74, 1237–1242 (2011)Google Scholar
  13. 13.
    Narendra, K., Wright, E., Mason, L.: Applications of Learning Automata to Telephone Traffic Routing and Control. IEEE Transactions on Systems, Man, and Cybernetics, 785–792 (1977)Google Scholar
  14. 14.
    Narendra, K., Viswanathan, R.: A Two-Level System of Schotastic Automata for Periodic Random Environments. IEEE Transactions on Systems, Man, and Cybernetics, 285–289 (1972)Google Scholar
  15. 15.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: an autocatalytic optimizing process, Technical Report TR91-016, Politecnico di Milano (1991)Google Scholar
  16. 16.
    Dorigo, M.: Optimization, learning and natural algorithms. PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Milan (1992)Google Scholar
  17. 17.
    Dorigo, M., Blum, C.: Ant colony optimization theory: A survey. Theoretical Computer Science 344(2-3), 243–278 (2005)MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Javier de Lope
    • 1
    • 2
  • Darío Maravall
    • 1
  • Yadira Quiñonez
    • 1
  1. 1.Computational Cognitive Robotics Group, Dept. Artificial IntelligenceUniversidad Politécnica de MadridSpain
  2. 2.Dept. Applied Intelligent SystemsUniversidad Politécnica de MadridSpain

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