Game Theory Models for Multi-Robot Patrolling of Infraestructures

Hernandez Serrato, Erik; Cerro Giner, Jaime del y Barrientos Cruz, Antonio (2013). Game Theory Models for Multi-Robot Patrolling of Infraestructures. "International Journal of Advanced Robotic Systems", v. 10 (n. 181-20); pp. 1-9. ISSN 1729-8806.


Título: Game Theory Models for Multi-Robot Patrolling of Infraestructures
  • Hernandez Serrato, Erik
  • Cerro Giner, Jaime del
  • Barrientos Cruz, Antonio
Tipo de Documento: Artículo
Título de Revista/Publicación: International Journal of Advanced Robotic Systems
Fecha: 2013
Volumen: 10
Palabras Clave Informales: Multi‐Robot Patrolling, Game Theory, Experience‐Weighted
Escuela: Centro de Automática y Robótica (CAR) UPM-CSIC
Departamento: Automática, Ingeniería Electrónica e Informática Industrial [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Abstract This work is focused on the problem of performing multi‐robot patrolling for infrastructure security applications in order to protect a known environment at critical facilities. Thus, given a set of robots and a set of points of interest, the patrolling task consists of constantly visiting these points at irregular time intervals for security purposes. Current existing solutions for these types of applications are predictable and inflexible. Moreover, most of the previous centralized and deterministic solutions and only few efforts have been made to integrate dynamic methods. Therefore, the development of new dynamic and decentralized collaborative approaches in order to solve the aforementioned problem by implementing learning models from Game Theory. The model selected in this work that includes belief‐based and reinforcement models as special cases is called Experience‐Weighted Attraction. The problem has been defined using concepts of Graph Theory to represent the environment in order to work with such Game Theory techniques. Finally, the proposed methods have been evaluated experimentally by using a patrolling simulator. The results obtained have been compared with previous available

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ID de Registro: 14732
Identificador DC:
Identificador OAI:
Identificador DOI: 10.5772/55535
URL Oficial:
Depositado por: Antonio Barrientos
Depositado el: 03 Abr 2013 10:29
Ultima Modificación: 21 Abr 2016 14:32
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