Probabilistic Control Architecture
Here, we define a behaviour-based architecture that uses the probabilistic activation of a set of basic behaviours, B. These behaviours, such as the attraction or repulsion from light sources, are designed independently of the pattern to be formed.
In our studies, each basic behaviour creates a mapping from the sensory perception to one of the three actuators (movement, light and gripper) of the s-bot . The basic behaviour set B is partitioned into three, Bm, Bl and Bg, based on the actuator it acts on. These subsets contain basic behaviours that are mutually exclusive, i.e., only one from each subset can be executed at each time step. Behaviours belonging to different subsets can be executed in parallel, since they control different actuators.
The movement behaviours (Bm) are:
The light behaviours (Bl) are:
The gripper behaviours (Bg) are:
All three subsets also have a null behaviour that keeps the state of an actuator unchanged. Given the set of basic behaviours, the design of a control system for the swarm-bot to create a certain pattern is then reduced to the design of two parts. First, the context function, h, maps the sensor readings onto a set of contexts that are designed for the particular pattern. This mapping is a categorisation of the sensory perception of the s-bot. Second, the activation probability matrix, P, determines the probability that a given basic behaviour is activated in a given context. In this matrix each row refers to a different context and each column to a particular basic behaviour. The elements of the matrix store the activation probabilities.
Once a behaviour is implemented by specifying the context function and the activation probability matrix, then at each time step the context of the s-bot is computed. Using this context, the probability of activating each behaviour is obtained from the matrix. Then, one behaviour from each subset is chosen based on the activation probabilities at random, and executed in parallel.
The architecture proposed provides a separation of the task-dependent part of the behaviour from the remaining parts that are independent from the task. Therefore, the context function h and the activation probability matrix P fully define the overall behaviour of an s-bot.
We describe here the probabilistic control architecture applied to the cluster formation. We will describe the context function h, showing for each context a typical situation. Then, we will show the probability matrix that allows to obtain the final behavior.
In each figure, the s-bot in lighter color indicates the one the context is referred to.
Activation Probability Matrix :
The activation probability matrix is shown in the following table. The parameter P refers to the probability of switching on the light, which is used to tune the number of cluster and the cluster sizes in the environment. A null value result in no clustering, because there is no attraction to sound. Low values enable the formation of a single, rather dynamic, structure. High value result in the formation of multiple clusters.
|Swarm-bots project started
on October 1,2001
|The project terminated
on March 31, 2005.
Sat, 21 Aug 2010 12:46:15 -0500