  The information available to robots in real tasks is widely distributed both
in time and space, requiring the agent to search for relevant data. In humans,
that face the same problem when sounds, images and smells are presented to
their sensors in a daily scene, a natural system is applied: Attention. As
vision plays an important role in our routine, most research regarding
attention has involved this sensorial system and the same has been replicated
to the robotics field. However,most of the robotics tasks nowadays do not rely
only in visual data, that are still costly. To allow the use of attentive
concepts with other robotics sensors that are usually used in tasks such as
navigation, self-localization, searching and mapping, a generic attentional
model has been previously proposed. In this work, feature mapping functions
were designed to build feature maps to this attentive model from data from
range scanner and sonar sensors. Experiments were performed in a high fidelity
simulated robotics environment and results have demonstrated the capability of
the model on dealing with both salient stimuli and goal-driven attention over
multiple features extracted from multiple sensors.
