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robot soccer
MAPS (Multi-Agent Planning System) is a system for multi-agent coordination that has been developed in the robot soccer domain.
      
This enables the robots to perform several different task such as navigation, map generation or intelligent group behavior and does not limit them to the game of robot soccer.
      
This paper surveys recent trends in developing a micro-robot soccer system, and presents a design procedure for soccer-playing robots, focusing on our system based on the centralized approach.
      
The robot soccer game has a lot of challenging problems, such as coordination between robots, motion planning of robots, visual recognition of objects, and so on.
      
We made a LEGO Mindstorms robot soccer model using a distributed behaviour-based system, which was showcased at RoboCup98 during the soccer World Cup in France 1998.
      
To show the feasibility of the proposed method, experimental results of a robot soccer game will be used for illustration.
      
The complete approach is implemented in a complex simulated robot soccer environment.
      
The AGILO Robot Soccer Team-Experience-Based Learning and Probabilistic Reasoning in Autonomous Robot Control
      
This article describes the computational model underlying the AGILO autonomous robot soccer team, its implementation, and our experiences with it.
      
The paper discusses the computational techniques based on experimental data from the 2001 robot soccer world championship.
      
Some theoretical and practical advances based on this model are described, including experiments with the constraint-based design of robot soccer players.
      
A controller for our robot soccer team, UBC Dynamo98, has been modeled in CN, and implemented in Java, using the Java Beans architecture.
      
Detailed empirical results are presented comparing and contrasting these three approaches on two versions of a complex task, namely robot soccer keepaway, that differ in difficulty of learning.
      
Although the algorithm is able to classify 32 different colors, only 13 are enough for the purpose of the robot soccer game.
      
An approach toward establishing a fast and robust vision system for the purpose of a robot soccer game is presented.
      
Although these ideas have been illustrated for robot soccer, we believe that they can usefully be applied to a wide range of multiagent systems.
      
As we are particularly interested in domains such as robot soccer with dynamically balancing robots, dynamics is a critical feature.
      
As with robot soccer, the game follows a trajectory through state space.
      
At the same time this robot soccer system is used as a test bed to experiment with different kinds of techniques.
      
By contrast, some vision systems used by other robot soccer teams employ local image processing to obtain the desired frame rate of vision system.
      
 

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