Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.

Quiñonez Carrillo, Alma Yadira; Maravall Gomez-Allende, Darío y Lope Asiaín, Javier de (2013). Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.. "Robotics and Autonomous Systems", v. 61 (n. 7); pp. 714-720. ISSN 0921-8890. https://doi.org/10.1016/j.robot.2012.07.008.

Descripción

Título: Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.
Autor/es:
  • Quiñonez Carrillo, Alma Yadira
  • Maravall Gomez-Allende, Darío
  • Lope Asiaín, Javier de
Tipo de Documento: Artículo
Título de Revista/Publicación: Robotics and Autonomous Systems
Fecha: Julio 2013
Volumen: 61
Materias:
Palabras Clave Informales: Multi-robot systems, Bio-inspired threshold models, Stochastic learning automata, Multi-task distribution, Self-coordination of multiple robots, Multi-heterogeneous specialized task distribution, Sistemas multi-robot, Modelos de umbral bio-inspirado, aprendizaje autómata estocástico, distribución multitarea, Autocoordinación de robots múltiples, 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-selection of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested in a decentralized solution where the robots themselves autonomously and in an individual manner, are responsible for 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-task distribution problem and we propose a solution using two different approaches by applying Response Threshold Models as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithms, 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: 21243
Identificador DC: http://oa.upm.es/21243/
Identificador OAI: oai:oa.upm.es:21243
Identificador DOI: 10.1016/j.robot.2012.07.008
URL Oficial: http://www.sciencedirect.com/science/article/pii/S092188901200111X
Depositado por: Memoria Investigacion
Depositado el: 13 Nov 2013 15:39
Ultima Modificación: 21 Abr 2016 11:23
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