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

Quiñonez Carrillo, Alma Yadira; Lope Asiaín, Javier de y Maravall Gomez-Allende, Darío (2012). Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata. En: "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.

Descripción

Título: Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata
Autor/es:
  • Quiñonez Carrillo, Alma Yadira
  • Lope Asiaín, Javier de
  • Maravall Gomez-Allende, Darío
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 7th International Conference, HAIS 2012
Fechas del Evento: 28/03/2012 - 30/03/2012
Lugar del Evento: Salamanca, España
Título del Libro: Hybrid Artificial Intelligent Systems
Fecha: 2012
ISBN: 978-3-642-28941-5
Volumen: 7208
Materias:
Palabras Clave Informales: 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.
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 21090
Identificador DC: http://oa.upm.es/21090/
Identificador OAI: oai:oa.upm.es:21090
Identificador DOI: 10.1007/978-3-642-28942-2_10
URL Oficial: http://link.springer.com/chapter/10.1007%2F978-3-642-28942-2_10
Depositado por: Memoria Investigacion
Depositado el: 12 Nov 2013 16:12
Ultima Modificación: 21 Abr 2016 11:11
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