Arquitectura y diseño de un sistema completo de navegación semántica. Descripción de su ontología y gestión de conocimiento

Mención Internacional en el título de doctorEn el actual mundo de la robótica, existe una tendencia general de implementar mecanismos conductuales basados en la psicología humana tomando como ejemplo el procesamiento natural del pensamiento. Esto permite una mayor comprensión del entorno y de los objetos que contiene. Esta tesis se desarrolla con la idea subyacente de conseguir que un robot se comporte de manera semejante a como lo haría un ser humano. Esto no es algo inusual, muchos investigadores dedican su esfuerzo a crear sistemas que resuelven una determinada tarea de una manera similar a como lo resolvería una persona. En el caso de esta tesis, la tarea o habilidad sobre la que se ha trabajado ha sido la navegación de robots móviles. En el caso de la navegación es necesario que se procesen una serie de subtareas de cuya eficiencia va a depender que el robot llegue realmente al lugar al que quería ir. Se necesita un planificador que decida los lugares a los que se debe dirigir el robot. Y si esas decisiones se toman en base a una clasificación razonada del entorno, que se realiza en función de una serie de conceptos que definen el mundo que rodea al robot, se está realizando una navegación a un nivel más abstracto que el geométrico o topológico, más cercano a la manera de proceder de un ser humano. En esta tesis, este nivel de navegación es referido como navegación semántica, tal como otros autores han hecho anteriormente. En este punto, se puede definir que el objetivo principal de esta tesis es diseñar e implementar un sistema de navegación semántica que gestione una mayor cantidad de conceptos y relaciones, al mismo tiempo que se estudian las implementaciones más eficientes. Esto permite clasificar de una manera precisa el entorno y dota de mayor autonomía al robot a la hora de encontrar rutas que le acerquen a su destino. Habiendo enmarcado la tesis dentro de este objetivo, se ha trabajado sobre el diseño de una ontología para definir el entorno. Todos los navegadores necesitan manejar un mapa que es interpretado por el planificador y que además sirve para que el robot se localice en el mundo que le rodea. En el caso de la navegación semántica, la información representada en esta ontología funciona como mapa semántico y cumple con esta función. El diseño de la ontología también es determinante para el sistema de razonamiento que necesita incorporar el navegador. Este sistema de razonamiento es el que se encarga de extraer la información necesaria a partir de los datos de los que se dispone y que se almacenan como entidades de la ontología. La navegación semántica requiere además que se incorporen al robot varios subsistemas que aporten distintas habilidades. La capacidad sensorial es una de estas habilidades. Debido a las características de este tipo de navegación, la capacidad sensorial es mucho más exigente que en otros navegadores puesto que el robot necesita percibir los objetos del entorno. Otra habilidad destacable es la capacidad de interacción con los usuarios humanos. Además, se necesita un sistema de navegación geométrico o topológico para el bajo nivel, ya que la navegación semántica ha sido tratada como una capa superior de abstracción que se construye sobre otro navegador. En esta tesis se detalla cómo funciona el sistema navegador completo, el desarrollo de los subsistemas y la integración de todos ellos. La arquitectura del sistema ha sido diseñada para ser modular, con lo que se ha podido implementar varias soluciones diferentes para todos los módulos. Esto ha llevado a integrar varios sistemas de percepción, probar con distintos sistemas de razonamiento, dos tipos de navegadores (uno geométrico y otro topológico) y dos interfaces de usuario (una por teclado y otra basada en un sistema de diálogo con voz). Esto sugiere que el sistema de navegación desarrollado en esta tesis es a su vez una buena herramienta para analizar y comparar los subsistemas por separado. La gestión de la información semántica es otra de las cuestiones relevantes en esta tesis. Por ello quedan descritos los procedimientos mediante los que se obtiene la información necesaria para el razonador. Se contempla la recursividad de destinos, puesto que la respuesta a una petición de destino puede ser desconocida, pero sucesivas iteraciones pueden llevar a un destino conocido. Además, se han estudiado varias maneras de ampliar la información que tiene el sistema. Esto es, por lo tanto, aprender. Las vías de aprendizaje contempladas, aparte de la introducción manual de datos, son la clasificación autónoma de estancias, consultas en webs de conocimiento y la interacción con humanos mediante diálogo. La capacidad de adquisición de nuevos conceptos o relaciones por parte del robot, está directamente relacionada con su autonomía. El robot no necesita de la intervención humana si es capaz de aprender nuevos conceptos por sí mismo. Una vez explicados los sistemas desarrollados en esta tesis, se describen una serie de experimentos bien documentados. Se han incluido pruebas de integración, pruebas de subsistemas individuales, pruebas de aprendizaje y pruebas de razonamiento.In the current robotics, there is a general tendency to implement behavioral mechanisms based on human psychology, taking the natural processing of thought as an example. This provides a greater understanding of the environment and the contained objects. This thesis is developed with the underlying idea of getting a robot to behave in a similar way as a human would. This is not unusual, many researchers dedicate their effort to create systems that solve a certain task in a manner similar as a person solve it. The focused task or skill in this thesis is the navigation of mobile robots. In the case of navigation, processing a series of subtasks is necessary. The subtask efficiency influences on the capability of the robot to reach the desired place. A planner is needed to decide where the robot should be go. In this thesis the navigation has a level of abstraction higher than in geometric or topological navigation. The decisions of the planner are made based on a reasoned classification of the environment. The classification is made according to the concepts that define the world that surrounds the robot. This navigation is closer to the way of a human being. In this thesis, this level of navigation is named semantic navigation, as other authors have named it before. The main objective of this thesis is to design and implement a semantic navigation system that manages a greater number of concepts and relationships. In addition, the most efficient implementations have been studied. This allows a precise classification of the environment. This also gives the robot greater autonomy to find routes that bring it closer to its destination. The design of an ontology to define the environment is presented. All navigators need to manage a map that the planner interprets. In addition, the map is used for the robot to be located in the world around it. In the case of semantic navigation, the information represented in this ontology functions as a semantic map and fulfills these functions. The design of the ontology is determinant for the reasoning system that needs to incorporate the navigator. This system of reasoning is responsible for extracting the necessary information from the data available. The data are stored as entities of the ontology. Semantic navigation requires that several subsystems that contribute different skills are incorporated into the robot. Sensory ability is one such skill. Due to the characteristics of this type of navigation, the sensorial capacity is much more demanding than in other navigation systems. This is because the robot needs to perceive the objects in the environment. Another remarkable skill is the ability to interact with human users. In addition, a geometric or topological navigation system is required for the low level, since semantic navigation has been treated as an upper layer of abstraction that is built on another navigation system. This thesis details the complete operation of the navigation system, the development of the subsystems and the integration of all of them. The system architecture has been designed to be modular. For this reason it has been possible to implement several different solutions for all the modules. This has led to the integration of several perception systems, to test different systems of reasoning, two types of browsers (one geometric and one topological) and two user interfaces (one by keyboard and another based on a dialog system with voice). This suggests that the navigation system developed in this thesis is also a good tool to analyze and compare the subsystems separately. The management of semantic information is another relevant issue in this thesis. Therefore, the procedures by which the necessary information for the reasoner is obtained are described. Recursion of destinations is contemplated, since the response to a destination request may be unknown, but successive iterations can lead to a known destination. In addition, several ways of acquiring new information have been studied. The paths of learning contemplated are the manual introduction of data, the autonomous classification of stays, consultations in knowledge webs and the interaction with humans through dialogue. Once the systems developed in this thesis have been explained, a set of well-documented experiments are described. Integration tests, individual subsystem tests, learning tests and reasoning tests have been included.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Luis Enrique Moreno Lorente.- Secretario: Martin Stoelen.- Vocal: Fernando Matía Espad

Object Detection Techniques Applied on Mobile Robot Semantic Navigation

The future of robotics predicts that robots will integrate themselves more every day with human beings and their environments. To achieve this integration, robots need to acquire information about the environment and its objects. There is a big need for algorithms to provide robots with these sort of skills, from the location where objects are needed to accomplish a task up to where these objects are considered as information about the environment. This paper presents a way to provide mobile robots with the ability-skill to detect objets for semantic navigation. This paper aims to use current trends in robotics and at the same time, that can be exported to other platforms. Two methods to detect objects are proposed, contour detection and a descriptor based technique, and both of them are combined to overcome their respective limitations. Finally, the code is tested on a real robot, to prove its accuracy and efficiency.The research leading to these results has received funding from the ARCADIA project DPI2010-21047-C02-01, funded by CICYT project grant on behalf of Spanish Ministry of Economy and Competitiveness and from the RoboCity2030-II project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Object detection applied to indoor environments for mobile robot navigation

To move around the environment, human beings depend on sight more than their other senses, because it provides information about the size, shape, color and position of an object. The increasing interest in building autonomous mobile systems makes the detection and recognition of objects in indoor environments a very important and challenging task. In this work, a vision system to detect objects considering usual human environments, able to work on a real mobile robot, is developed. In the proposed system, the classification method used is Support Vector Machine (SVM) and as input to this system, RGB and depth images are used. Different segmentation techniques have been applied to each kind of object. Similarly, two alternatives to extract features of the objects are explored, based on geometric shape descriptors and bag of words. The experimental results have demonstrated the usefulness of the system for the detection and location of the objects in indoor environments. Furthermore, through the comparison of two proposed methods for extracting features, it has been determined which alternative offers better performance. The final results have been obtained taking into account the proposed problem and that the environment has not been changed, that is to say, the environment has not been altered to perform the tests.The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and co-funded by Structural Funds of the EU and NAVEGASE-AUTOCOGNAV project (DPI2014-53525-C3-3-R), funded by Ministerio de Economía y competitividad of Spain

Semantic information for robot navigation: a survey

There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situationsThe research leading to these results has received funding from HEROITEA: Heterogeneous 480 Intelligent Multi-Robot Team for Assistance of Elderly People (RTI2018-095599-B-C21), funded by Spanish 481 Ministerio de Economía y Competitividad. The research leading to this work was also supported project "Robots sociales para estimulacón física, cognitiva y afectiva de mayores"; funded by the Spanish State Research Agency under grant 2019/00428/001. It is also funded by WASP-AI Sweden; and by Spanish project Robotic-Based Well-Being Monitoring and Coaching for Elderly People during Daily Life Activities (RTI2018-095599-A-C22)

A topological navigation system for indoor environments based on perception events

The aim of the work presented in this article is to develop a navigation system that allows a mobile robot to move autonomously in an indoor environment using perceptions of multiple events. A topological navigation system based on events that imitates human navigation using sensorimotor abilities and sensorial events is presented. The increasing interest in building autonomous mobile systems makes the detection and recognition of perceptions a crucial task. The system proposed can be considered a perceptive navigation system as the navigation process is based on perception and recognition of natural and artificial landmarks, among others. The innovation of this work resides in the use of an integration interface to handle multiple events concurrently, leading to a more complete and advanced navigation system. The developed architecture enhances the integration of new elements due to its modularity and the decoupling between modules. Finally, experiments have been carried out in several mobile robots, and their results show the feasibility of the navigation system proposed and the effectiveness of the sensorial data integration managed as events.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU and NAVEGASE-AUTOCOGNAV project (DPI2014-53525-C3-3-R) and funded by Ministerio de Economía y competitividad of SPAIN

A Semantic Labeling of the Environment Based on What People Do

In this work, a system is developed for semantic labeling of locations based on what people do. This system is useful for semantic navigation of mobile robots. The system differentiates environments according to what people do in them. Background sound, number of people in a room and amount of movement of those people are items to be considered when trying to tell if people are doing different actions. These data are sampled, and it is assumed that people behave differently and perform different actions. A support vector machine is trained with the obtained samples, and therefore, it allows one to identify the room. Finally, the results are discussed and support the hypothesis that the proposed system can help to semantically label a room.The research leading to these results has received funding from the RoboCity2030-III-CMproject (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+Den la Comunidad de Madrid and cofunded by Structural Funds of the EU and NAVEGASEAUTOCOGNAVproject (DPI2014-53525-C3-3-R), funded by Ministerio de Economía y Competitividad of Spain.The research leading to these results has received funding from the RoboCity2030-III-CMproject (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+Den la Comunidad de Madrid and cofunded by Structural Funds of the EU and NAVEGASEAUTOCOGNAVproject (DPI2014-53525-C3-3-R), funded by Ministerio de Economía y Competitividad of Spain

Revisiting the epidemiology of bloodstream infections and healthcare-associated episodes: results from a multicentre prospective cohort in Spain (PRO-BAC Study)

PROBAC REIPI/GEIH-SEIMC/SAEI Group.The epidemiology of bloodstream infections (BSIs) is dynamic as it depends on microbiological, host and healthcare system factors. The aim of this study was to update the information regarding the epidemiology of BSIs in Spain considering the type of acquisition. An observational, prospective cohort study in 26 Spanish hospitals from October 2016 through March 2017 including all episodes of BSI in adults was performed. Bivariate analyses stratified by type of acquisition were performed. Multivariate analyses were performed by logistic regression. Overall, 6345 BSI episodes were included; 2510 (39.8%) were community-acquired (CA), 1661 (26.3%) were healthcare-associated (HCA) and 2056 (32.6%) hospital-acquired (HA). The 30-day mortality rates were 11.6%, 19.5% and 22.0%, respectively. The median age of patients was 71 years (interquartile range 60–81 years) and 3656 (58.3%; 95% confidence interval 57.1–59.6%) occurred in males. The proportions according to patient sex varied according to age strata. Escherichia coli (43.8%), Klebsiella spp. (8.9%), Staphylococcus aureus (8.9%) and coagulase-negative staphylococci (7.4%) were the most frequent pathogens. Multivariate analyses confirmed important differences between CA and HCA episodes, but also between HCA and HA episodes, in demographics, underlying conditions and aetiology. In conclusion, we have updated the epidemiological information regarding patients’ profiles, underlying conditions, frequency of acquisition types and aetiological agents of BSI in Spain. HCA is confirmed as a distinct type of acquisition.This work was financed by grants from Plan Nacional de I+D+i 2013–2016, Instituto de Salud Carlos III, Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Ciencia, Innovación y Universidades [PI16/01432] and the Spanish Network for Research in Infectious Diseases (REIPI) [RD16/0016/0001; RD16/0016/0008], co‐financed by the European Development Regional Fund ‘A way to achieve Europe’, Operative program Intelligent Growth 2014–2020

RuVa: A runtime software variability algorithm

Context-aware and smart systems that require runtime reconfiguration to cope with changes in the environment increasingly demand variability management mechanisms that can address runtime concerns. In recent years, we have witnessed new dynamic variability solutions using dynamic software product line (DSPL) approaches. However, while few solutions proposed so far have addressed the need to add, change and remove variants dynamically, none of them provide a way to check the constraints between features at runtime. Because all SAT solvers perform variability constraint checking in off-line mode, we suggest in this ongoing research paper the integration of RuVa, a runtime variability algorithm, with the FaMa tool suite to check feature constraints dynamically before a new feature is added or an existing feature is removed. This research suggests a novel approach to modifying the variability model of context-aware systems dynamically and check the feature constraints on the fly. We integrate our solution with a SAT solver that can be invoked at runtime by a cyber-physical system. We validate the effectiveness and performance of the proposed algorithm using simulations. We also provide a proof-of-concept for updating the configuration of a robot's variability model based on contextual changes

Pseudomonas aeruginosa Community-Onset Bloodstream Infections: Characterization, Diagnostic Predictors, and Predictive Score Development—Results from the PRO-BAC Cohort

Community-onset bloodstream infections (CO-BSI) caused by gram-negative bacilli are common and associated with significant mortality; those caused by Pseudomonas aeruginosa are associated with worse prognosis and higher rates of inadequateempirical antibiotic treatment. The aims of this study were to describe the characteristics of patients with CO-BSI caused by P. aeruginosa, to identify predictors, and to develop a predictive score for P. aeruginosa CO-BSI. Materials/methods: PROBAC is a prospective cohort including patients >14 years with BSI from 26 Spanish hospitals between October 2016 and May 2017. Patients with monomicrobial P. aeruginosa CO-BSI and monomicrobial Enterobacterales CO-BSI were included. Variables of interest were collected. Independent predictors of Pseudomonas aeruginosa CO-BSI were identified by logistic regression and a prediction score was developed. Results: A total of 78patients with P. aeruginosa CO-BSI and 2572 with Enterobacterales CO-BSI were included. Patients with P. aeruginosa had a median age of 70 years (IQR 60–79), 68.8% were male, median Charlson score was 5 (IQR 3–7), and 30-daymortality was 18.5%. Multivariate analysis identified the following predictors of CO-BSI-PA [adjusted OR (95% CI)]: male gender [1.89 (1.14–3.12)], haematological malignancy [2.45 (1.20–4.99)], obstructive uropathy [2.86 (1.13–3.02)], source of infection other than urinary tract, biliary tract or intra-abdominal [6.69 (4.10–10.92)] and healthcare-associated BSI [1.85 (1.13–3.02)]. Anindex predictive of CO-BSI-PA was developed; scores ≥ 3.5 showed a negative predictive value of 89% and an area under the receiver operator curve (ROC) of 0.66. Conclusions: We did not find a good predictive score of P. aeruginosa CO-BSI due to its relatively low incidence in the overall population. Our model includes variables that are easy to collect in real clinical practice and could be useful to detect patients with very low risk of P. aeruginosa CO-BSI