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)


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.


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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© 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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