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Fuzzy-Neural-Genetic Layered Multi-Agent Reactive Control of Robotic Soccer

  • Andon V. Topalov
  • Spyros G. Tzafestas
Part of the Massive Computing book series (MACO, volume 3)

Abstract

Robotic soccer belongs to the class of multi-agent systems and involves many challenging sub-problems. Teams of robotic players have to cooperate in order to put the ball in the opposing goal and at the same time defend their own goal. This paper is concerned with the problem of learning two basic reactive behaviors of robotic agents playing soccer, namely: (i) learning to intercept the moving ball while avoiding collisions with other players and play field walls, and (ii) learning to shoot the ball toward the goal or pass it in a desired direction. The approach adopted has a “layered structure”, i.e. the ball interception/obstacle avoidance (BIOA) behavior is first learned, and the skills obtained are then employed to learn the shooting ball (SB) behavior at a higher layer.

The proposed control scheme involves a fuzzy-neural trajectory generator (FNTG), which supplies data to a trajectory-tracking controller (TTC) consisting of a conventional PD feedback controller (CFC) followed by a fuzzy-neural controller (FNC). This allows the implementation of the robot behaviors (tasks) at a trajectory generator level using off-line learning and the robot kinematics model only. The complete dynamics of the mobile base is taken into account by the TTC, and it’s learning is performed on a real mobile robot. Considering the advantages of genetic algorithms (GAs), a GA approach is employed to perform the learning process of the FNTG layers. The overall system (including the play field) was simulated in the MATLAB® environment and the results obtained are very encouraging showing the effectiveness of the proposed layered fuzzy-neural-genetic learning control scheme.

Keywords

Mobile Robot Multiagent System Obstacle Avoidance Trajectory Generator Mobile Base 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Andon V. Topalov
    • 1
  • Spyros G. Tzafestas
    • 2
  1. 1.Department of Control SystemsTechnical University of Sofia — branch PlovdivPlovdivBulgaria
  2. 2.Intelligent Robotics and Automation Laboratory, Computer Science Division, Department of Electrical and Computer EngineeringNational Technical University of AthensZografou, AthensGreece

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