Enhancing smart environments with mobile robots

Sensor networks are becoming popular nowadays in the development of smart environments. Heavily relying on static sensor and actuators, though, such environments usually lacks of versatility regarding the provided services and interaction capabilities. Here we present a framework for smart environments where a service robot is included within the sensor network acting as a mobile sensor and/or actuator. Our framework integrates on-the-shelf technologies to ensure its adaptability to a variety of sensor technologies and robotic software. Two pilot cases are presented as evaluation of our proposal.

Modelado orientado a objetos del sistema de control de la presión arterial en pacientes sometidos al tratamiento de hemodiálisis

Se ha realizado el modelado orientado a objetos del sistema de control cardiovascular en situaciones de diálisis aplicando una analogía eléctrica en el que se emplean componentes conectados mediante interconexiones. En este modelado se representan las ecuaciones diferenciales del sistema cardiovascular y del sistema de control barorreceptor así como las ecuaciones dinámicas del intercambio de fluidos y solutos del sistema hemodializador. A partir de este modelo se ha realizado experiencias de simulación en condiciones normales y situaciones de hemorragias, transfusiones de sangre y de ultrafiltración e infusión de fluido durante tratamiento de hemodiálisis. Los resultados obtenidos muestran en primer lugar la efectividad del sistema barorreceptor para compensar la hipotensión arterial inducida por los episodios de hemorragia y transfusión de sangre. En segundo lugar se muestra la respuesta del sistema de control ante diferentes tasas de ultrafiltración durante la hemodiálisis y se sugieren valores óptimos para la adecuada operación.

Probability and Common-Sense: Tandem Towards Robust Robotic Object Recognition in Ambient Assisted Living

The suitable operation of mobile robots when providing Ambient Assisted Living (AAL) services calls for robust object recognition capabilities. Probabilistic Graphical Models (PGMs) have become the de-facto choice in recognition systems aiming to e ciently exploit contextual relations among objects, also dealing with the uncertainty inherent to the robot workspace. However, these models can perform in an inco herent way when operating in a long-term fashion out of the laboratory, e.g. while recognizing objects in peculiar con gurations or belonging to new types. In this work we propose a recognition system that resorts to PGMs and common-sense knowledge, represented in the form of an ontology, to detect those inconsistencies and learn from them. The utilization of the ontology carries additional advantages, e.g. the possibility to verbalize the robot's knowledge. A primary demonstration of the system capabilities has been carried out with very promising results.