* IntroducciónUno de los principales retos de la robótica en la actualidad es conseguir que los robots realicen robustamente tareas útiles en entornos no triviales. Para la gran mayoría de tareas es necesario no sólo que sean capaces de modelar los objetos que haya en el entorno e interactuar con ellos sino también con humanos (comunicándose con ellos y aceptando órdenes). Todo esto implica que han de ser capaces de percibir el entorno autónoma y eficientemente, de tal forma que les sirva para poder completar exitosamente las misiones que se les encomienden.Para poder realizar tareas que no puedan ser completadas de una forma meramente reactiva es necesario que los robots generen algún tipo de representación del entorno. Para tareas simples como la navegación local o global estas estructuras pueden ser extremadamente simples, como mapas de rejillas bidimensionales. Sin embargo, cuando se espera que los robots desarrollen tareas complejas se hace necesario que estos modelos sean considerablemente ricos y estructurados, con una carga semántica mayor que una simple rejilla de ocupación.Con los avances de la robótica, es cada vez más viable que los robots habiten entornos reales no controlados, donde las sombras, zonas de baja textura, oclusiones y obstáculos representan importantes dificultades tanto para percibir el mundo como para realizar acciones sobre él. A pesar de que estos entornos son más complejos y menos predecibles que un entorno controlado, ni están compuestos de formas aleatorias ni cambian sin razón. Para construir robots verdaderamente inteligentes, es necesario que estos usen toda la información ecológica disponible. Esta información es aun más importante en entornos de interior, donde las habitaciones y los objetos que en ellas se pueden encontrar están en su mayor parte sujetos a pequeños conjuntos de formas geométricas y, en muchos casos, mantienen relaciones estructurales fuertes (e.g., de composición, donde un elemento del entorno está siempre compuesto por otros; de localización, donde un elemento siempre se encuentra dentro o al lado de otro. Por tanto, es preciso disponer de la tecnología que nos permita crear robots que modelen e interaccionen con su entorno usando la información a priori de la que se disponga sobre el entorno de una forma eficiente, que permita a los robots actuar con el de una forma apropiada.*Desarrollo teóricoEl objetivo de este trabajo es el estudio y desarrollo de técnicas novedosas que puedan ser usadas para suplir estas necesidades. Las aportaciones de la tesis se pueden clasificar en tres bloques: a) ingeniería de software orientada a la robótica; b) modelado y percepción activa; y c) experimentos. En concreto, el grueso de la tesis se centra en el segundo y tercero de los bloques.Dentro de la ingeniería de software orientada a la robótica destaca una parte importante del desarrollo de RoboComp, un framework de robótica. RoboComp destaca por ser orientado a componentes, por el conjunto de herramientas y librerías que lo forman, y por hacer un uso extensivo de lenguajes específicos de dominio (DSL) que facilitan y aceleran el desarrollo de software para robots. Entre las herramientas desarrolladas destacan RCManager (un gestor gráfico de redes de componentes), RCMonitor (un monitorizador y un replicador dela salida de componentes), RCControlPanel (una herramienta que sirve para acceder y controlar simultáneamente varios componentes de robots) y RCInnerModelSimulator (un simulador de robótica). También son dignos de mención InnerModel e InnerModelViewer, un par de clases diseñadas para representar, manejar y visualizar árboles cinemáticos; y IMDSL, un lenguaje específico de dominio usado para describir estos árboles.En el campo de la percepción activa se presenta Active Grammar-based Modeling (AGM), una arquitectura que representa la principal contribución de la tesis. Active Grammar-based Modeling se basa en el hecho de que los cambios que tienen lugar en los modelos del mundo que generan y usan los robots pueden ser descritos dentro de una gramátoca formal con ciertas características especiales. Estas gramáticas, junto con la asociación de las reglas que las componen con diferentes configuraciones de sus comportamientos físicos y perceptivos (siendo estos últimos planteados como procesos de muestreo estocástico) aportan soluciones a distintos problemas o fenómenos perceptivos: la generación de modelos, la selección de acción (tanto para la percepción como para cumplir un objetivo determinado), la percepción encubierta y la inclusión de restricciones perceptivas dependientes del contexto.* ConclusiónAGM propone un planteamiento distribuido donde varios agentes interactúan de acuerdo a la gramática que rige el control del robot, haciendo que éste interactúe con el entorno y modificando entre todos el modelo del mundo. Un nodo central se encarga de gestionar los cambios (permitiendo únicamente aquellos que no violan la gramática) y coordinar al resto de agentes. 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