In 1992, the programming language PDL was developed by Steels and Vertommen for the stimulus driven control of autonomous agents [5]. This language has been used by a number of groups working in behavior oriented robotics [4]. It allows the description of parallel processes that react to sensor readings by influencing the actuators. Many primitive behaviors, like taxis, are easily formulated in such a framework. On the other hand, it is difficult to implement more complex behaviors in PDL that need information about slow changes in the environment. The Dual Dynamics control architecture, developed by Herbert Jäger [3], describes reactive behaviors in a hierarchy of control processes. Each layer of the system is partitioned into two modules: the activation dynamics that determines whether or not a behavior tries to influence actuators, and the target dynamics, that determines strength and direction of that influence. The different levels of the hierarchy correspond to different time scales. The higher level behaviors configure the lower level control loops via activation factors that determine the mode in which the primitive behaviors are. These can produce qualitatively different reactions if the agent encounters the same stimulus again, but has changed its mode due to stimuli that it saw in the meantime.
Our control architecture is based on these ideas, as shown in Figure 1. A more detailed description is given in [1]. The robots are controlled in closed loops that use different time scales. We extend the Dual Dynamics scheme by introducing a third dynamics, namely the perceptual dynamics shown on the left side. Here, either slow changing physical sensors are plugged in at the higher levels, or the readings of fast changing sensors, like the ball position, are aggregated by dynamic processes to slower and longer lasting percepts. Since we use a subsampling in time, we can afford to implement an increasing number of sensors, behaviors and actuators in the higher layers without an explosion of computational costs.
The behaviors are constructed in a bottom up fashion: First, the processes that should react quickly to fast changing stimuli are designed. Their critical parameters, e.g. a mode parameter or a target position, are determined. When the fast processes work reliably with constant parameters, the next level can be added to the system. This level can now influence the environment either directly by moving slow actuators or indirectly by changing the critical parameters of the processes in the lower level.
Each of our robots is controlled autonomously from the lower levels of the hierarchy using a local view to the world. For instance, we present the angle and the distance to the ball and the nearest obstacle to each agent. In the upper layers of the control system the focus changes. Now we regard the team as the individual. It has a slow changing global view to the playground and coordinates the robots as its extremities to reach strategic goals.
