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An autonomous robot is usually expected to perform its assigned task in complex and unstructured environments, where the high amount of sensory information acquired by sensors cannot be entirely processed in real-time. Like in living organisms, an attention mechanism is required in order to select most relevant stimuli from the environment. The outcome of this selection process comprises all the information that will be available to the control system of the robot. Therefore,an optimal operation of the attention mechanism is necessary for robot mission success. In this work, we present a novel cognitive architecture which permits the implementation and integration of efficient attention mechanisms for autonomousrobots. Additionally, the application of this approach to the domain of unknown environment exploration is discussed and results are analyzed. Specifically, the influence of attention in the generation of efficient robot behavior is demonstrated.