UBA-Sot : An Approach to Control and Team Strategy in Robot Soccer

  • Santos, Juan-Miguel (Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria) ;
  • Scolnik, Hugo-Daniel (Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria) ;
  • Ignacio Laplagne (Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria) ;
  • Sergio Daicz (Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria) ;
  • Flavio Scarpettini (Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria) ;
  • Hector Fassi (Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria) ;
  • Claudia Castelo (Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria)
  • Published : 2003.03.01

Abstract

In this study, we introduce the main ideas on the control and strategy used by the robot soccer team of the Universidad de Buenos hires, UBA-Sot. The basis of our approach is to obtain a cooperative behavior, which emerges from homogeneous sets of individual behaviors. Except for the goalkeeper, the behavior set of each robot contains a small number of individual behaviors. Basically, the individual behaviors have the same core: to move from the initial to-ward the target coordinates. However, these individual behaviors differ because each one has a different precondition associated with it. Each precondition is the combination of a number of elementary ones. The aim of our approach is to answer the following questions: How can the robot compute the preconditions in time\ulcorner How are the control actions defined, which allow the robot to move from the initial toward the final coordinates\ulcorner The way we cope with these issues is, on the one hand, to use ball and robot predictors and, on the other hand, to use very fast planning. Our proposal is to use planning in such a way that the behavior obtained is closer to a reactive than a deliberative one. Simulations and experiments on real robots, based on this approach, have so far given encouraging results.

Keywords

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