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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.
Título: | Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems. |
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Autor/es: |
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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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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.
ID de Registro: | 21243 |
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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 |