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Descripción
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| Multi-robot systems are one of the most challenging problems in autonomous robots. Teams of homogeneous or heterogeneous robots must be able to solve complex tasks. Sometimes the tasks have a cooperative basis in which the global objective is shared by all the robots. In other situations, the robots can be different and even contradictory goals, defining a kind of competitive problems. The multi-robot systems domain is a perfect example in which the uncertainty and vagueness in sensor readings and robot odometry must be handled by using techniques which can deal with this kind of imprecise data. In this paper we introduce the use of Reinforcement Learning techniques for solving cooperative problems in teams of homogeneous robots. As an example, the problem of maintaining a mobile robots formation is studied. | |
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Internacional
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Si |
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DOI
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http://dx.doi.org/10.1007/978-3-540-87656-4_77 |
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Edición del Libro
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0 |
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Editorial del Libro
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Springer |
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ISBN
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978-3-540-87655-7 |
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Serie
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LNCS 5271 |
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Título del Libro
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Hybrid Artificial Intelligence Systems |
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Desde página
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625 |
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Hasta página
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632 |