The ``behavior based'' approach has proved useful for real time control of mobile robots. Here, the actions of an agent are derived directly from sensory input without requiring an explicit symbolic model of the world [1,2,5]. In 1992, the programming language PDL was developed by Steels and Vertommen As a tool to implement stimulus driven control of autonomous agents [8,9]. PDL has been used by several groups working in behavior oriented robotics [7]. It allows the description of parallel processes that react to sensor readings by influencing the actuators. Many basic behaviors, like taxis, are easily formulated in such a framework. On the other hand, it is difficult and expensive to implement more complex behaviors in PDL, mostly those that need persistent percepts about the state of the environment. Consider for example a situation in which we want to position our defensive players preferentially on the side of the field where the offensive players of the other team mostly concentrate. It is not useful to take this decision based on a snapshot of sensor readings. The positioning of the defense has to be determined only from time to time, e.g. every minute, on the basis of the average positions of the attacking robots during the immediate past. The Dual Dynamics control architecture, developed by Herbert Jäger [3,4], arranges reactive behaviors in a hierarchy of control processes. Each layer of the system is partitioned into two modules: the activation dynamics that determines at every time step whether or not a behavior tries to influence actuators, and the target dynamics, that describes strength and direction of that influence. The different levels of the hierarchy correspond to different time scales. The high-level behaviors configure the low-level control loops via activation factors that set the current mode of the primitive behaviors. This can produce qualitatively different reactions if the agent receives the same stimulus again, but has changed of mode due to stimuli received in the meantime.
The remainder of the paper is organized as follows: The next section describes the mechanical and electrical design of our RoboCup F180 league robots. Then the vision and communication systems are presented. In Section 5 we explain the hierarchical control architecture that we use to generate behaviors for the game of soccer and illustrate it using examples from the RoboCup domain.
