Global Localization based on Evolutionary Optimization Algorithms for Indoor and Underground Environments

Mención Internacional en el título de doctorA fully autonomous robot is defined by its capability to sense, understand and move within the environment to perform a specific task. These qualities are included within the concept of navigation. However, among them, a basic transcendent one is localization, the capacity of the system to know its position regarding its surroundings. Therefore, the localization issue could be defined as searching the robot’s coordinates and rotation angles within a known environment. In this thesis, the particular case of Global Localization is addressed, when no information about the initial position is known, and the robot relies only on its sensors. This work aims to develop several tools that allow the system to locate in the two most usual geometric map representations: occupancy maps and Point Clouds. The former divides the dimensional space into equally-sized cells coded with a binary value distinguishing between free and occupied space. Point Clouds define obstacles and environment features as a sparse set of points in the space, commonly measured through a laser sensor. In this work, various algorithms are presented to search for that position through laser measurements only, in contrast with more usual methods that combine external information with motion information of the robot, odometry. Therefore, the system is capable of finding its own position in indoor environments, with no necessity of external positioning and without the influence of the uncertainty that motion sensors typically induce. Our solution is addressed by implementing various stochastic optimization algorithms or Meta-heuristics, specifically those bio-inspired or commonly known as Evolutionary Algorithms. Inspired by natural phenomena, these algorithms are based on the evolution of a series of particles or population members towards a solution through the optimization of a cost or fitness function that defines the problem. The implemented algorithms are Differential Evolution, Particle Swarm Optimization, and Invasive Weed Optimization, which try to mimic the behavior of evolution through mutation, the movement of swarms or flocks of animals, and the colonizing behavior of invasive species of plants respectively. The different implementations address the necessity to parameterize these algorithms for a wide search space as a complete three-dimensional map, with exploratory behavior and the convergence conditions that terminate the search. The process is a recursive optimum estimation search, so the solution is unknown. These implementations address the optimum localization search procedure by comparing the laser measurements from the real position with the one obtained from each candidate particle in the known map. The cost function evaluates this similarity between real and estimated measurements and, therefore, is the function that defines the problem to optimize. The common approach in localization or mapping using laser sensors is to establish the mean square error or the absolute error between laser measurements as an optimization function. In this work, a different perspective is introduced by benefiting from statistical distance or divergences, utilized to describe the similarity between probability distributions. By modeling the laser sensor as a probability distribution over the measured distance, the algorithm can benefit from the asymmetries provided by these divergences to favor or penalize different situations. Hence, how the laser scans differ and not only how much can be evaluated. The results obtained in different maps, simulated and real, prove that the Global Localization issue is successfully solved through these methods, both in position and orientation. The implementation of divergence-based weighted cost functions provides great robustness and accuracy to the localization filters and optimal response before different sources and noise levels from sensor measurements, the environment, or the presence of obstacles that are not registered in the map.Lo que define a un robot completamente autónomo es su capacidad para percibir el entorno, comprenderlo y poder desplazarse en ´el para realizar las tareas encomendadas. Estas cualidades se engloban dentro del concepto de la navegación, pero entre todas ellas la más básica y de la que dependen en buena parte el resto es la localización, la capacidad del sistema de conocer su posición respecto al entorno que lo rodea. De esta forma el problema de la localización se podría definir como la búsqueda de las coordenadas de posición y los ángulos de orientación de un robot móvil dentro de un entorno conocido. En esta tesis se aborda el caso particular de la localización global, cuando no existe información inicial alguna y el sistema depende únicamente de sus sensores. El objetivo de este trabajo es el desarrollo de varias herramientas que permitan que el sistema encuentre la localización en la que se encuentra respecto a los dos tipos de mapa más comúnmente utilizados para representar el entorno: los mapas de ocupación y las nubes de puntos. Los primeros subdividen el espacio en celdas de igual tamaño cuyo valor se define de forma binaria entre espacio libre y ocupado. Las nubes de puntos definen los obstáculos como una serie dispersa de puntos en el espacio comúnmente medidos a través de un láser. En este trabajo se presentan varios algoritmos para la búsqueda de esa posición utilizando únicamente las medidas de este sensor láser, en contraste con los métodos más habituales que combinan información externa con información propia del movimiento del robot, la odometría. De esta forma el sistema es capaz de encontrar su posición en entornos interiores sin depender de posicionamiento externo y sin verse influenciado por la deriva típica que inducen los sensores de movimiento. La solución se afronta mediante la implementación de varios tipos de algoritmos estocásticos de optimización o Meta-heurísticas, en concreto entre los denominados bio-inspirados o comúnmente conocidos como Algoritmos Evolutivos. Estos algoritmos, inspirados en varios fenómenos de la naturaleza, se basan en la evolución de una serie de partículas o población hacia una solución en base a la optimización de una función de coste que define el problema. Los algoritmos implementados en este trabajo son Differential Evolution, Particle Swarm Optimization e Invasive Weed Optimization, que tratan de imitar el comportamiento de la evolución por mutación, el movimiento de enjambres o bandas de animales y la colonización por parte de especies invasivas de plantas respectivamente. Las distintas implementaciones abordan la necesidad de parametrizar estos algoritmos para un espacio de búsqueda muy amplio como es un mapa completo, con la necesidad de que su comportamiento sea muy exploratorio, así como las condiciones de convergencia que definen el fin de la búsqueda ya que al ser un proceso recursivo de estimación la solución no es conocida. Estos algoritmos plantean la forma de buscar la localización ´optima del robot mediante la comparación de las medidas del láser en la posición real con lo esperado en la posición de cada una de esas partículas teniendo en cuenta el mapa conocido. La función de coste evalúa esa semejanza entre las medidas reales y estimadas y por tanto, es la función que define el problema. Las funciones típicamente utilizadas tanto en mapeado como localización mediante el uso de sensores láser de distancia son el error cuadrático medio o el error absoluto entre distancia estimada y real. En este trabajo se presenta una perspectiva diferente, aprovechando las distancias estadísticas o divergencias, utilizadas para establecer la semejanza entre distribuciones probabilísticas. Modelando el sensor como una distribución de probabilidad entorno a la medida aportada por el láser, se puede aprovechar la asimetría de esas divergencias para favorecer o penalizar distintas situaciones. De esta forma se evalúa como difieren las medias y no solo cuanto. Los resultados obtenidos en distintos mapas tanto simulados como reales demuestran que el problema de la localización se resuelve con éxito mediante estos métodos tanto respecto al error de estimación de la posición como de la orientación del robot. El uso de las divergencias y su implementación en una función de coste ponderada proporciona gran robustez y precisión al filtro de localización y gran respuesta ante diferentes fuentes y niveles de ruido, tanto de la propia medida del sensor, del ambiente y de obstáculos no modelados en el mapa del entorno.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Fabio Bonsignorio.- Secretario: María Dolores Blanco Rojas.- Vocal: Alberto Brunete Gonzále

Wildfire spreading simulator using fast marching algorithm

Programs that can predict wildfire behavior are a very useful tool in terms of extinguishing these fires more effectively. State of the art wildfire simulators present some drawbacks such as not being sufficiently user-friendly, being expensive, requiring great computational power or having poor graphical representation. This paper presents a prototype wildfire simulation app that uses Fast Marching (FM) as its core algorithm. The wildfire app is developed as a Matlab GUI. Said application shows the shape of the fire front at a given moment in time in a 3D map of the terrain affected by the fire. Any real life maps can be loaded to the application for wildfire prediction. The user can choose to vary parameters such as starting (ignition) and ending points, wind direction and speed and propagation time, and see its effect on fire propagation. Interface response to each change in the input is very fast, therefore proving the effciency of the algorithm. Although a prototype, the wildfire basic app is superior to some state of the art simulators regarding certain important features. It can be concluded that Fast Marching is a valid core algorithm for a fire simulator. The way the app is programmed in Matlab confers it flexibility, enabling further specific changes that make it truly competitive against currently used wildfire simulators

Using the Jensen-Shannon, density power, and Itakura-Saito divergences to implement an evolutionary-based global localization filter for mobile robots

One of the most demanding skills for a mobile robot is to be intelligent enough to know its own location. The global localization problem consists of obtaining the robot's pose (position and orientation) in a known map if the initial location is unknown. This task is addressed applying evolutionary computation concepts (Differential Evolution). In the current approach, the distances obtained from the laser sensors are combined with the predicted scan (in the known map) from possible locations to implement a cost function that is optimized by an evolutionary filter. The laser beams (sensor information) are modeled using a combination of probability distributions to implement a non-symmetric fitness function. The main contribution of this paper is to apply the probabilistic approach to design three different cost functions based on known divergences (Jensen-Shannon, Itakura-Saito, and density power). The three metrics have been tested in different experiments and the localization module performance is exceptional in regions with occlusions caused by different obstacles. This fact validates that the non-symmetric probabilistic approach is a suitable technique to be applied to multiple metrics.This work was supported by the Competitive Improvement of Drilling and Blasting Cycle in Mining and Underground-Works through New Techniques of Engineering, Explosives, Prototypes, and Advanced Tools, which is a Research and Development project undertaken by the following companies: Obras Subterr a neas, MaxamCorp Holding, Putzmeister Iberica, Subterra Ingenieria, Expace On Boards Systems, Dacartec Servicios Informaticos, and Cepasa Ensayos Geotecnicos

COPD prevalence and hospital admissions in Galicia (Spain). An analysis using the potential of new health information systems

Introduction and objectives: Chronic obstructive pulmonary disease (COPD) is a major public health problem. The aim of this study was to ascertain the prevalence of COPD and whethersuch prevalence was positively or negatively associated with COPD admissions, using all thedata of a regional health care system.Materials and methods: We designed a descriptive cross-sectional study which included all sub-jects aged over 45 years, diagnosed with COPD in primary care in 2013. We also calculated the number of such patients who had a record of hospital admissions due to this disease. COPDprevalence and incidence of admissions were calculated. Poisson regression models were thenused to analyse the association between cases with diagnosis of COPD and admissions due toCOPD, by sex, adjusting for socio-demographic variables and distance to hospital. Sensitivitysubanalyses were performed by reference to the respective municipal rurality indices.Results: Median municipal prevalence of COPD was 5.29% in men and 2.19% in women. Amongpatients with COPD, 28.22% of men and 16.00% of women had at least one hospital admission.The relative risk of admission per unit of the standardised prevalence ratio was 0.37 (95% CI0.34---0.41) for men and 0.39 (95% CI 0.34---0.45) for women.Conclusions: There is a significant negative association between COPD prevalence and hospital admissions due to this disease. The proportion of admissions is lower in municipalities lyingfurthest from hospitals. There is considerable municipal variability in terms of COPD preva-lence and proportion of admissions. In-depth attention should be given to disease-managementtraining programmesS

A genome-wide association study follow-up suggests a possible role for PPARG in systemic sclerosis susceptibility

Introduction: A recent genome-wide association study (GWAS) comprising a French cohort of systemic sclerosis (SSc) reported several non-HLA single-nucleotide polymorphisms (SNPs) showing a nominal association in the discovery phase. We aimed to identify previously overlooked susceptibility variants by using a follow-up strategy.<p></p> Methods: Sixty-six non-HLA SNPs showing a P value <10-4 in the discovery phase of the French SSc GWAS were analyzed in the first step of this study, performing a meta-analysis that combined data from the two published SSc GWASs. A total of 2,921 SSc patients and 6,963 healthy controls were included in this first phase. Two SNPs, PPARG rs310746 and CHRNA9 rs6832151, were selected for genotyping in the replication cohort (1,068 SSc patients and 6,762 healthy controls) based on the results of the first step. Genotyping was performed by using TaqMan SNP genotyping assays. Results: We observed nominal associations for both PPARG rs310746 (PMH = 1.90 × 10-6, OR, 1.28) and CHRNA9 rs6832151 (PMH = 4.30 × 10-6, OR, 1.17) genetic variants with SSc in the first step of our study. In the replication phase, we observed a trend of association for PPARG rs310746 (P value = 0.066; OR, 1.17). The combined overall Mantel-Haenszel meta-analysis of all the cohorts included in the present study revealed that PPARG rs310746 remained associated with SSc with a nominal non-genome-wide significant P value (PMH = 5.00 × 10-7; OR, 1.25). No evidence of association was observed for CHRNA9 rs6832151 either in the replication phase or in the overall pooled analysis.<p></p> Conclusion: Our results suggest a role of PPARG gene in the development of SSc

Clinical characteristics and outcome of Spanish patients with ANCA-associated vasculitides Impact of the vasculitis type, ANCA specificity, and treatment on mortality and morbidity

The aim of this study was to describe the clinical characteristics of ANCA-associated vasculitides (AAV) at presentation, in a wide cohort of Spanish patients, and to analyze the impact of the vasculitis type, ANCA specificity, prognostic factors, and treatments administered at diagnosis, in the outcome. A total of 450 patients diagnosed between January 1990 and January 2014 in 20 Hospitals from Spain were included. Altogether, 40.9% had granulomatosis with polyangiitis (GPA), 37.1% microscopic polyangiitis (MPA), and 22% eosinophilic granulomatosis with polyangiitis (EGPA). The mean age at diagnosis was 55.6±17.3 years, patients with MPA being significantly older (P<0.001). Fever, arthralgia, weight loss, respiratory, and ear-nose-throat (ENT) symptoms, were the most common at disease onset. ANCAs tested positive in 86.4% of cases: 36.2% C-ANCA-PR3 and 50.2% P-ANCA-MPO. P-ANCA-MPO was significantly associated with an increased risk for renal disease (OR 2.6, P<0.001) and alveolar hemorrhage (OR 2, P=0.010), while C-ANCA-PR3 was significantly associated with an increased risk for ENT (OR 3.4, P<0.001) and ocular involvement (OR 2.3, P=0.002). All patients received corticosteroids (CS) and 74.9% cyclophosphamide (CYC). The median follow-up was 82 months (IQR 100.4). Over this period 39.9% of patients suffered bacterial infections and 14.6% opportunistic infections, both being most prevalent in patients with highcumulated doses of CYC and CS (P<0.001). Relapses were recorded in 36.4% of cases with a mean rate of 2.5±2.3, and were more frequent in patients with C-ANCA-PR3 (P=0.012). The initial disease severity was significantly associated with mortality but not with the occurrence of relapses. One hundred twenty-nine (28.7%) patients (74 MPA, 41 GPA, 14 EGPA) died. The mean survival was 58 months (IQR 105) and was significantly lower for patients with MPA (P<0.001). Factors independently related to death were renal involvement (P=0.010), cardiac failure (P=0.029) and age over 65 years old (P<0.001) at disease onset, and bacterial infections (P<0.001). An improved outcome with significant decrease in mortality and treatment-related morbidity was observed in patients diagnosed after 2000, and was related to the implementation of less toxic regimens adapted to the disease activity and stage, and a drastic reduction in the cumulated CYC and CS dose

Efficacy and safety of native versus pegylated Escherichia coli asparaginase for treatment of adults with high-risk, Philadelphia chromosome-negative acute lymphoblastic leukemia

Native or pegylated (PEG) asparaginase (ASP) are commonly used in treatment of acute lymphoblastic leukemia (ALL), but have been scarcely compared in the same trial in adult patients. Native vs. PEG-ASP administered according to availability in each center were prospectively evaluated in adults with high-risk ALL. Ninety-one patients received native ASP and 35 PEG-ASP in induction. No significant differences were observed in complete remission, minimal residual disease levels after induction and after consolidation, disease-free survival, and overall survival. No significant differences in grades 3–4 toxicity were observed in the induction period, although a trend for higher hepatic toxicity was observed in patients receiving PEG-ASP. In this trial the type of ASP did not influence patient response and outcome.Supported in part with the grants PI10/01417 from Fondo de Investigaciones Sanitarias and RD12/0036/0029 from RTICC, Instituto de Salud Carlos III, 2014 SGR225(GRE), CERCA Program, Generalitat de Catalunya, Spain, and a funding from ‘La Caixa’ Foundation

Chemotherapy or allogeneic transplantation in high-risk Philadelphia chromosome–negative adult lymphoblastic leukemia

The need for allogeneic hematopoietic stem cell transplantation (allo-HSCT) in adults with Philadelphia chromosome–negative (Ph−) acute lymphoblastic leukemia (ALL) with high-risk (HR) features and adequate measurable residual disease (MRD) clearance remains unclear. The aim of the ALL-HR-11 trial was to evaluate the outcomes of HR Ph− adult ALL patients following chemotherapy or allo-HSCT administered based on end-induction and consolidation MRD levels. Patients aged 15 to 60 years with HR-ALL in complete response (CR) and MRD levels (centrally assessed by 8-color flow cytometry) <0.1% after induction and <0.01% after early consolidation were assigned to receive delayed consolidation and maintenance therapy up to 2 years in CR. The remaining patients were allocated to allo-HSCT. CR was attained in 315/348 patients (91%), with MRD <0.1% after induction in 220/289 patients (76%). By intention-to-treat, 218 patients were assigned to chemotherapy and 106 to allo-HSCT. The 5-year (±95% confidence interval) cumulative incidence of relapse (CIR), overall survival (OS), and event-free survival probabilities for the whole series were 43% ± 7%, 49% ± 7%, and 40% ± 6%, respectively, with CIR and OS rates of 45% ± 8% and 59% ± 9% for patients assigned to chemotherapy and of 40% ± 12% and 38% ± 11% for those assigned to allo-HSCT, respectively. Our results show that avoiding allo-HSCT does not hamper the outcomes of HR Ph− adult ALL patients up to 60 years with adequate MRD response after induction and consolidation. Better postremission alternative therapies are especially needed for patients with poor MRD clearance

Cross-disease Meta-analysis of Genome-wide Association Studies for Systemic Sclerosis and Rheumatoid Arthritis Reveals IRF4 as a New Common Susceptibility Locus

Objectives: Systemic sclerosis (SSc) and rheumatoid arthritis (RA) are autoimmune diseases that share clinical and immunological characteristics. To date, several shared SSc- RA loci have been identified independently. In this study, we aimed to systematically search for new common SSc-RA loci through an inter-disease meta-GWAS strategy. Methods: We performed a meta-analysis combining GWAS datasets of SSc and RA using a strategy that allowed identification of loci with both same-direction and opposingdirection allelic effects. The top single-nucleotide polymorphisms (SNPs) were followed-up in independent SSc and RA case-control cohorts. This allowed us to increase the sample size to a total of 8,830 SSc patients, 16,870 RA patients and 43,393 controls. Results: The cross-disease meta-analysis of the GWAS datasets identified several loci with nominal association signals (P-value < 5 x 10-6), which also showed evidence of association in the disease-specific GWAS scan. These loci included several genomic regions not previously reported as shared loci, besides risk factors associated with both diseases in previous studies. The follow-up of the putatively new SSc-RA loci identified IRF4 as a shared risk factor for these two diseases (Pcombined = 3.29 x 10-12). In addition, the analysis of the biological relevance of the known SSc-RA shared loci pointed to the type I interferon and the interleukin 12 signaling pathways as the main common etiopathogenic factors. Conclusions: Our study has identified a novel shared locus, IRF4, for SSc and RA and highlighted the usefulness of cross-disease GWAS meta-analysis in the identification of common risk loci