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Stochastic Learning Automata for Self-coordination in Heterogeneous Multi-Tasks Selection in Multi-Robot Systems

  • Yadira Quiñonez
  • Darío Maravall
  • Javier de Lope
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7094)

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, as opposed to the usual multi-tasks allocation problem in multi-robot systems in which an external controller distributes the existing tasks among the individual robots. In this work we are considering a specifically distributed or decentralized approach in which 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 and we propose a solution through automata learning-based probabilistic algorithm, to solve the corresponding multi-tasks distribution problem. The paper ends with a critical discussion of experimental results.

Keywords

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

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yadira Quiñonez
    • 1
  • Darío Maravall
    • 1
  • Javier de Lope
    • 1
  1. 1.Computational Cognitive Robotics, Dept. Artificial IntelligenceUniversidad Politécnica de MadridSpain

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