  We present an active visual search model for finding objects in unknown
environments. The proposed algorithm guides the robot towards the sought object
using the relevant stimuli provided by the visual sensors. Existing search
strategies are either purely reactive or use simplified sensor models that do
not exploit all the visual information available. In this paper, we propose a
new model that actively extracts visual information via visual attention
techniques and, in conjunction with a non-myopic decision-making algorithm,
leads the robot to search more relevant areas of the environment. The attention
module couples both top-down and bottom-up attention models enabling the robot
to search regions with higher importance first.
  The proposed algorithm is evaluated on a mobile robot platform in a 3D
simulated environment. The results indicate that the use of visual attention
significantly improves search, but the degree of improvement depends on the
nature of the task and the complexity of the environment. In our experiments,
we found that performance enhancements of up to 42\% in structured and 38\% in
highly unstructured cluttered environments can be achieved using visual
attention mechanisms.
