Analysis and design of local positioning systems for localizing drones over bounded regions

[ES] El uso de los UAV (Unmanned-Aerial-Vehicles) ha crecido significativamente en los últimos años. Su inserción en el sector civil abre paso a su implementación en el sector agrícola, industrial, así como su uso para aplicaciones de vigilancia o reparto. No obstante, el desarrollo eficiente de estas aplicaciones depende de la capacidad del dron de posicionarse de forma autónoma. Si bien es común encontrar drones con sistemas de posicionamiento satelital (GNSS), estos sistemas resultan insuficientes para la navegación autónoma en entornos urbanos o de interiores. En estos escenarios, la implementación de sistemas de posicionamiento local (LPS) resulta de gran interés por su capacidad de adaptación. A través de la distribución óptima de las balizas que constituyen este sistema pueden adaptarse a la mayoría de entornos, así como mejorar sus prestaciones. No obstante, la complejidad de este problema se ha caracterizado como NP-Hard, lo que dificulta su resolución. En este Trabajo de Fin de Máster se desarrolla un algoritmo genético para optimizar LPS en diferentes entornos. Este algoritmo, innovador en el diseño de LPS para UAV, se prueba sobre un entorno urbano diseñado. Los resultados obtenidos denotan la validez de la metodología al obtener incertidumbres en la localización significativamente menores que los GNSS, siendo además substancial la mejoría introducida por el algoritmo genético diseñado.[EN] Unmanned-Aerial-Vehicles (UAV) widespread use have grown significantly in recent years. Their insertion in the civil sector allows their implementation in the agricultural and industrial sectors, as well as their use for surveillance or delivery applications. However, the efficient development of these applications depends on the drone’s ability to position itself autonomously. Although it is common to find drones with satellite positioning systems (GNSS), these systems are insufficient for autonomous navigation in urban or indoor environments. In these scenarios, the implementation of local positioning systems (LPS) is widely spread due to their adaptability capabilities. Through the optimal distribution of the sensors that constitute this system, they can adapt to almost any environment while also improving its performance. However, the complexity of this problem has been characterized as NP-Hard, which complicates its resolution. In this Master’s Final Project, a genetic algorithm is developed to optimize LPS in different environments. This algorithm, pioneer in the design of LPS for UAV localization, is tested on a generated urban environment. The results obtained denote the effectiveness of the methodology by obtaining location uncertainties significantly lower than GNSS. Moreover, the proposed genetic algorithm achieves greater results than non-optimized distributions, proving its capabilities

Optimal COVID-19 Adapted Table Disposition in Hostelry for Guaranteeing the Social Distance through Memetic Algorithms

[EN] The COVID-19 pandemic has challenged all physical interactions. Social distancing, face masks and other rules have reshaped our way of living during the last year. The impact of these measures for indoor establishments, such as education or hostelry businesses, resulted in a considerable organisation problem. Achieving a table distribution inside these indoor spaces that fulfilled the distancing requirements while trying to allocate the maximum number of tables for enduring the pandemic has proved to be a considerable task for multiple establishments. This problem, defined as the Table Location Problem (TLP), is categorised as NP-Hard, thus a metaheuristic resolution is recommended. In our previous works, a Genetic Algorithm (GA) optimisation was proposed for optimising the table distribution in real classrooms. However, the proposed algorithm performed poorly for high obstacle density scenarios, especially when allocating a considerable number of tables due to the existing dependency between adjacent tables in the distance distribution. Therefore, in this paper, we introduce for the first time, to the authors’ best knowledge, a Memetic Algorithm (MA) optimisation that improves the previously designed GA through the introduction of a Gradient Based Local Search. Multiple configurations have been analysed for a real hostelryrelated scenario and a comparison between methodologies has been performed. Results show that the proposed MA optimisation obtained adequate solutions that the GA was unable to reach, demonstrating a superior convergence performance and an overall greater flexibility. The MA performance denoted its value not only from a COVID-19 distancing perspective but also as a flexible managing algorithm for daily table arrangement, thus fulfilling the main objectives of this paper.SIMinisterio de Ciencia, Innovación y Universidade

Analysis of reliable deployment of TDOA local positioning architectures

.Local Positioning Systems (LPS) are supposing an attractive research topic over the last few years. LPS are ad-hoc deployments of wireless sensor networks for particularly adapt to the environment characteristics in harsh environments. Among LPS, those based on temporal measurements stand out for their trade-off among accuracy, robustness and costs. But, regardless the LPS architecture considered, an optimization of the sensor distribution is required for achieving competitive results. Recent studies have shown that under optimized node distributions, time-based LPS cumulate the bigger error bounds due to synchronization errors. Consequently, asynchronous architectures such as Asynchronous Time Difference of Arrival (A-TDOA) have been recently proposed. However, the A-TDOA architecture supposes the concentration of the time measurement in a single clock of a coordinator sensor making this architecture less versatile. In this paper, we present an optimization methodology for overcoming the drawbacks of the A-TDOA architecture in nominal and failure conditions with regards to the synchronous TDOA. Results show that this optimization strategy allows the reduction of the uncertainties in the target location by 79% and 89.5% and the enhancement of the convergence properties by 86% and 33% of the A-TDOA architecture with regards to the TDOA synchronous architecture in two different application scenarios. In addition, maximum convergence points are more easily found in the A-TDOA in both configurations concluding the benefits of this architecture in LPS high-demanded applicationS

Table Organization Optimization in Schools for Preserving the Social Distance during the COVID-19 Pandemic

[EN] The COVID-19 pandemic has supposed a challenge for education. The school closures during the initial coronavirus outbreak for reducing the infections have promoted negative effects on children, such as the interruption of their normal social relationships or their necessary physical activity. Thus, most of the countries worldwide have considered as a priority the reopening of schools but imposing some rules for keeping safe places for the school lessons such as social distancing, wearing facemasks, hydroalcoholic gels or reducing the capacity in the indoor rooms. In Spain, the government has fixed a minimum distance of 1.5 m among the students’ desks for preserving the social distancing and schools have followed orthogonal and triangular mesh patterns for achieving valid table dispositions that meet the requirements. However, these patterns may not attain the best results for maximizing the distances among the tables. Therefore, in this paper, we introduce for the first time in the authors’ best knowledge a Genetic Algorithm (GA) for optimizing the disposition of the tables at schools during the coronavirus pandemic. We apply this GA in two real-application scenarios in which we find table dispositions that increase the distances among the tables by 19.33% and 10%, respectively, with regards to regular government patterns in these classrooms, thus fulfilling the main objectives of the paper.SIMinisterio de Ciencia, Innovación y Universidade

Digital Twin for Automatic Transportation in Industry 4.0

[EN] Industry 4.0 is the fourth industrial revolution consisting of the digitalization of processes facilitating an incremental value chain. Smart Manufacturing (SM) is one of the branches of the Industry 4.0 regarding logistics, visual inspection of pieces, optimal organization of processes, machine sensorization, real-time data adquisition and treatment and virtualization of industrial activities. Among these tecniques, Digital Twin (DT) is attracting the research interest of the scientific community in the last few years due to the cost reduction through the simulation of the dynamic behaviour of the industrial plant predicting potential problems in the SM paradigm. In this paper, we propose a new DT design concept based on external service for the transportation of the Automatic Guided Vehicles (AGVs) which are being recently introduced for the Material Requirement Planning satisfaction in the collaborative industrial plant. We have performed real experimentation in two different scenarios through the definition of an Industrial Ethernet platform for the real validation of the DT results obtained. Results show the correlation between the virtual and real experiments carried out in the two scenarios defined in this paper with an accuracy of 97.95% and 98.82% in the total time of the missions analysed in the DT. Therefore, these results validate the model created for the AGV navigation, thus fulfilling the objectives of this paper.SIMinisterio de Ciencia, Innovación y Universidade

Hyperconnectivity Proposal for Smart Manufacturing

[EN] Smart Manufacturing is characterized by the digitization and massive communication of Cyber-Physical Systems under the Industrial Internet of Things paradigm. However, the heterogeneity of communication protocols hinders connectivity among assets due to lack of interoperability. Moreover, the decomposition of the classical production hierarchy towards decentralized self-organization makes the implementation of interoperability in industrial environments key to help decision-making. In this sense, the interoperability of heterogeneous assets (e.g., external, internal, and human) has been defined as hyperconnectivity and supposes a technological challenge in the scientific literature. To prove this novel hyperconnectivity definition, the authors propose and develop a novel hyperconnected demonstrator where all types of assets are interconnected in a case study consisting of the automation of an inspection process. For this purpose, an industrial internet platform has been used for connecting industrial equipment creating a collaborative environment through the use of interoperability. In this regard, it has been possible to communicate assets among the cloud, humans, and CPS with a processing time of less than 10 ms, which demonstrates that the technological challenge of implementing the hyperconnectivity concept of this paper has been successfully addressed.SIThis research has been developed and funded by the Spanish Ministry of Science and Innovation project grant number PID2019-108277GB-C21/AEI/10.13039/501100011033 and by the Universidad de León.Ministerio de Ciencia, Innovación y Universidade

Analysis of synchronous localization systems for UAVs urban applications

[EN] Unmanned-Aerial-Vehicles (UAVs) represent an active research topic over multiple fields for performing inspection, delivery and surveillance applications among other operations. However, achieving the utmost efficiency requires drones to perform these tasks without the need of human intervention, which demands a robust and accurate localization system for achieving a safe and efficient autonomous navigation. Nevertheless, currently used satellite-based localization systems like GPS are insufficient for high-precision applications, especially in harsh scenarios like indoor and deep urban environments. In these contexts, Local Positioning Systems (LPS) have been widely proposed for satisfying the localization requirements of these vehicles. However, the performance of LPS is highly dependent on the actual localization architecture and the spatial disposition of the deployed sensor distribution. Therefore, before the deployment of an extensive localization network, an analysis regarding localization architecture and sensor distribution should be taken into consideration for the task at hand. Nonetheless, no actual study is proposed either for comparing localization architectures or for attaining a solution for the Node Location Problem (NLP), a problem of NP-Hard complexity. Therefore, in this paper, we propose a comparison among synchronous LPS for determining the most suited system for localizing UAVs over urban scenarios. We employ the Cràmer–Rao-Bound (CRB) for evaluating the performance of each localization system, based on the provided error characterization of each synchronous architecture. Furthermore, in order to attain the optimal sensor distribution for each architecture, a Black-Widow-Optimization (BWO) algorithm is devised for the NLP and the application at hand. The results obtained denote the effectiveness of the devised technique and recommend the implementation of Time Difference Of Arrival (TDOA) over Time of Arrival (TOA) systems, attaining up to 47% less localization uncertainty due to the unnecessary synchronization of the target clock with the architecture sensors in the TDOA architecture.S

Experiencia docente con realidad aumentada para el diseño de máquinas

[ES] La mejora continua en las metodologías docentes requiere de innovación. Por este motivo, los docentes deben estar al tanto de las nuevas tendencias educativas, así como de los avances tecnológicos. Esto implica que los profesores deben implementar técnicas y herramientas novedosas en sus clases para mejorar la experiencia de la enseñanza. Por este motivo, se ha desarrollado una aplicación de Realidad Aumentada (RA) para que los alumnos aprendan a identificar los elementos mecánicos de una máquina. La aplicación de RA permite al alumno visualizar de forma tridimensional las piezas pudiendo identificar mejor las particularidades de estas. Además, con la aplicación de RA el alumno puede ver la pieza a tamaño real mediante un dispositivo móvil inteligente y un código impreso. Esta experiencia docente se ha realizado en la asignatura de diseño avanzado de máquinas en el máster universitario de ingeniería industrial con el objetivo de facilitar el proceso de aprendizaje al alumno mediante una experiencia inmersiva en una práctica síncrona de dos horas de duración. De acuerdo con los resultados obtenidos se ha mejorado en las pruebas de evaluación continua, así como el interés por parte del alumnado en utilizar estas técnicas. Por lo tanto, esta aplicación de RA demuestra la utilidad de poner en práctica herramientas innovadoras durante la enseñanza para mejorar el proceso educativo

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

Table Organization Optimization in Schools for Preserving the Social Distance during the COVID-19 Pandemic

The COVID-19 pandemic has supposed a challenge for education. The school closures during the initial coronavirus outbreak for reducing the infections have promoted negative effects on children, such as the interruption of their normal social relationships or their necessary physical activity. Thus, most of the countries worldwide have considered as a priority the reopening of schools but imposing some rules for keeping safe places for the school lessons such as social distancing, wearing facemasks, hydroalcoholic gels or reducing the capacity in the indoor rooms. In Spain, the government has fixed a minimum distance of 1.5 m among the students’ desks for preserving the social distancing and schools have followed orthogonal and triangular mesh patterns for achieving valid table dispositions that meet the requirements. However, these patterns may not attain the best results for maximizing the distances among the tables. Therefore, in this paper, we introduce for the first time in the authors’ best knowledge a Genetic Algorithm (GA) for optimizing the disposition of the tables at schools during the coronavirus pandemic. We apply this GA in two real-application scenarios in which we find table dispositions that increase the distances among the tables by 19.33% and 10%, respectively, with regards to regular government patterns in these classrooms, thus fulfilling the main objectives of the paper