An integrated model of visual attention for homecare robot with self-awareness

This paper proposes an integrated model of top-down and bottom-up visual attention with self-awareness for a homecare robot. For mimicking the human attention processes, a robot self-awareness model with fuzzy decision making system is developed and utilized, which is an important improvement on the existing robot attention models. Besides the task-driven object-based biasing, a robot self-awareness model can generate other parts of top-down biases in a robot visual attention model. Some results from the self-awareness model are obtained here. In order to update the weights in robot memory, the learning process is carried out through the Bayes´ rule. Three images are tested to evaluate the developed methods. Four types of saliency maps are compared to see how self-awareness can affect robot attention selection.