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

Quiñonez Carrillo, Alma Yadira and Maravall Gomez-Allende, Darío and 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.

Description

Title: Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.
Author/s:
  • Quiñonez Carrillo, Alma Yadira
  • Maravall Gomez-Allende, Darío
  • Lope Asiaín, Javier de
Item Type: Article
Título de Revista/Publicación: Robotics and Autonomous Systems
Date: July 2013
ISSN: 0921-8890
Volume: 61
Subjects:
Freetext Keywords: 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.
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-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.

More information

Item ID: 21243
DC Identifier: http://oa.upm.es/21243/
OAI Identifier: oai:oa.upm.es:21243
DOI: 10.1016/j.robot.2012.07.008
Official URL: http://www.sciencedirect.com/science/article/pii/S092188901200111X
Deposited by: Memoria Investigacion
Deposited on: 13 Nov 2013 15:39
Last Modified: 21 Apr 2016 11:23
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