Open document
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General information |
| Author |
Hernández, Erik; Cerro, Jaime del; Barrientos, Antonio |
| Published |
InTech Open Access Publisher, 2013
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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 work has tackled the patrolling problem with
centralized and deterministic solutions and only few
efforts have been made to integrate dynamic methods.
Therefore, one of the main contributions of this work is
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 approaches. |
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International Journal of Advanced Robotic Systems
Author: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Published: 2004
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