An Attentional Model for Autonomous Mobile Robots

The increase of applications that use autonomous robots has endowed them with a high number of sensors and actuators that are sometimes redundant. This new highly complex systems and the type of environment where they are expected to operate require them to deal with data overload and data fusion. In humans that face the same problem when sounds, images, and smells are presented to their sensors in a daily scene, a natural filter is applied: attention. Although there are many computational models that apply attentive systems to robotics, they usually are restricted to two classes of systems: 1) those that have complex biologically based attentional visual systems and 2) those that have simpler attentional mechanisms with a larger variety of sensors. This work proposes an attentional model inspired from biological systems and that supports a variety of robotics sensors. Furthermore, it discusses the possibility of using multiple sensors to define multiple features, with feature extraction modules that can handle exogenous and endogenous attentional processes. The experiments were performed in a simulated high-fidelity environment, and results have shown that the model proposed can account for detecting salient (bottom-up) and desired (top-down) stimuli according to the modeled features.