Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata

Quiñonez Carrillo, Alma Yadira and Lope Asiaín, Javier de and Maravall Gomez-Allende, Darío (2012). Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata. In: "7th International Conference, HAIS 2012", 28/03/2012 - 30/03/2012, Salamanca, España. ISBN 978-3-642-28941-5. pp. 103-114. https://doi.org/10.1007/978-3-642-28942-2_10.

Description

Title: Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata
Author/s:
  • Quiñonez Carrillo, Alma Yadira
  • Lope Asiaín, Javier de
  • Maravall Gomez-Allende, Darío
Item Type: Presentation at Congress or Conference (Article)
Event Title: 7th International Conference, HAIS 2012
Event Dates: 28/03/2012 - 30/03/2012
Event Location: Salamanca, España
Title of Book: Hybrid Artificial Intelligent Systems
Date: 2012
ISBN: 978-3-642-28941-5
Volume: 7208
Subjects:
Freetext 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, sistemas multi-robot, métodos estocásticos de aprendizaje autómata, Optimización de una colonia de hormigas, Distribución multitarea, Autocoordinación de robots múltiples, Refuerzo del aprendizaje, Distribución de tareas especializadas multi-heterogéneas.
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

More information

Item ID: 21090
DC Identifier: http://oa.upm.es/21090/
OAI Identifier: oai:oa.upm.es:21090
DOI: 10.1007/978-3-642-28942-2_10
Official URL: http://link.springer.com/chapter/10.1007%2F978-3-642-28942-2_10
Deposited by: Memoria Investigacion
Deposited on: 12 Nov 2013 16:12
Last Modified: 21 Apr 2016 11:11
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