Abstract
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.
This work has been partially funded by the Spanish Ministry of Science and Technology, project: DPI2006-15346-C03-02.
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Sanz, Y., de Lope, J., Martín H., J.A. (2008). Applying Reinforcement Learning to Multi-robot Team Coordination. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_77
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DOI: https://doi.org/10.1007/978-3-540-87656-4_77
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