Welcome to my site!
Saturday, 12 June 2004

ImageThis is the website of Ricardo Sanz, professor in systems engineering and automatic control and researcher in the field of autonomous systems.

In this site you will find information regarding my activitiy as well as other sources that may be of interest to you. Feel free to explore the site and to suggest any improvement to it.

I do most of my activity as part of the Autonomous Systems Laboratory. ASLab is a research group of ample interests ranging from conventional control and real-time systems to model-based engineering processes and artificial intelligence.

This last is, indeed, my main topic of interest; or to be more precise, I'm interested in mind theory, both artificial and natural within the long term engineering objective of systematically creating better machines by means of improving their intelligence.

Last Updated ( Saturday, 08 July 2017 )
A self-adaptation framework based on functional knowledge for augmented autonomy in robots
Thursday, 11 April 2013
Carlos Hernández, Julita Bermejo-Alonso and Ricardo Sanz
To appear in Integrated Computer-Aided Engineering
IOS Press through http://dx.doi.org/10.3233/ICA-180565

Robot control software endows robots with advanced capabilities for autonomous operation, such as navigation, object recognition or manipulation, in unstructured and dynamic environments. However, there is a steady need for more robust oper- ation, where robots should perform complex tasks by reliably exploiting these novel capabilities. Mission-level resilience is re- quired in the presence of component faults through failure recovery. To address this challenge, a novel self-adaptation framework based on functional knowledge for augmented autonomy is presented. A metacontroller is integrated on top of the robot control system, and it uses an explicit run-time model of the robot’s controller and its mission to adapt to operational changes. The model is grounded on a functional ontology that relates the robot’s mission with the robot’s architecture, and it is generated during the robot’s development from its engineering models. Advantages are discussed from both theoretical and practical viewpoints. An application example in a real autonomous mobile robot is provided. In this example, the generic metacontroller uses the robot’s functional model to adapt the control architecture to recover from a sensor failure.

A self-adaptation framework based on functional knowledge for augmented autonomy in robots. Carlos Hernández, Julita Bermejo-Alonso and Ricardo Sanz. Integrated Computer-Aided Engineering 2018, IOS Press

Article @ ASlab

Last Updated ( Thursday, 15 February 2018 )
It has always been models
Sunday, 30 July 2017
There is a relatively recent boom on model-based X. Model-based development, model-based design, model-based systems engineering, ...

In all the domains of engineering, it looks like we have just discovered the use of models to support our work. But this is, obviously, false. It has always been models. All around. All the time.

Last Updated ( Sunday, 30 July 2017 )
Ontologies as Backbone of Cognitive Systems Engineering
Friday, 10 March 2017
Ricardo Sanz, Julita Bermejo, Juan Morago and Carlos Hernández
INCOSE Cognition And OntologieS (CAOS) 2017

Cognitive systems are starting to be deployed as appliances across the technological landscape of modern societies. The increasing availability of high performance computing platforms has opened an opportunity for statistics-based cognitive systems that per- form quite as humans in certain tasks that resisted the symbolic methods of classic artificial intelligence. Cognitive artefacts appear every day in the media, raising a wave of mild fear concerning artificial intelligence and its impact on society. These systems, performance notwithstanding, are quite brittle and their reduced dependability limits their potential for massive deployment in mission-critical applications —e.g. in autonomous driving or medical diagnosis. In this paper we explore the actual possibility of building cognitive systems using engineering-grade methods that can assure the satisfaction of strict requirements for their operation. The final conclusion will be that, besides the potential improvement provided by a rigorous engineering process, we are still in need of a solid theory —possibly the main outcome of cognitive science— that could sustain such endeavour. In this sense, we propose the use of formal ontologies as back- bones of cognitive systems engineering processes and workflows.

Ontologies as Backbone of Cognitive Systems Engineering. Ricardo Sanz, Julita Bermejo, Juan Morago and Carlos Hernández. AISB Symposium on Cognition And OntologieS (CAOS) 2017

Draft paper @ ASLab

Last Updated ( Saturday, 11 March 2017 )
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