Information-Theoretic Control of Multiple Sensor Platforms

This thesis is concerned with the development of a consistent, information-theoretic basis for understanding of coordination and cooperation decentralised multi-sensor multi-platform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue mining or intelligent manufacturing. However, the effective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis. This thesis focuses on the problem of coordination and cooperation for teams of autonomous systems engaged in information gathering and data fusion tasks. While this is a subset of the general cooperative autonomous systems problem, it still encompasses a range of possible applications in picture compilation, navigation, searching and map building problems. The great advantage of restricting the domain of interest in this way is that an underlying mathematical model for coordination and cooperation can be based on the use of information-theoretic models of platform and sensor abilities. The information theoretic approach builds on the established principles and architecture previously developed for decentralised data fusion systems. In the decentralised control problem addressed in this thesis, each platform and sensor system is considered to be a distinct decision maker with an individual information-theoretic utility measure capturing both local objectives and the inter-dependencies among the decisions made by other members of the team. Together these information-theoretic utilities constitute the team objective. The key contributions of this thesis lie in the quantification and study of cooperative control between sensors and platforms using information as a common utility measure. In particular, * The problem of information gathering is formulated as an optimal control problem by identifying formal measures of information with utility or pay-off. * An information-theoretic utility model of coupling and coordination between decentralised decision makers is elucidated. This is used to describe how the information gathering strategies of a team of autonomous systems are coupled. * Static and dynamic information structures for team members are defined. It is shown that the use of static information structures can lead to efficient, although sub-optimal, decentralised control strategies for the team. * Significant examples in decentralised control of a team of sensors are developed. These include the multi-vehicle multi-target bearings-only tracking problem, and the area coverage or exploration problem for multiple vehicles. These examples demonstrate the range of non-trivial problems to which the theory in this thesis can be employed

Information-Theoretic Control of Multiple Sensor Platforms

This thesis is concerned with the development of a consistent, information-theoretic basis for understanding of coordination and cooperation decentralised multi-sensor multi-platform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue mining or intelligent manufacturing. However, the effective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis. This thesis focuses on the problem of coordination and cooperation for teams of autonomous systems engaged in information gathering and data fusion tasks. While this is a subset of the general cooperative autonomous systems problem, it still encompasses a range of possible applications in picture compilation, navigation, searching and map building problems. The great advantage of restricting the domain of interest in this way is that an underlying mathematical model for coordination and cooperation can be based on the use of information-theoretic models of platform and sensor abilities. The information theoretic approach builds on the established principles and architecture previously developed for decentralised data fusion systems. In the decentralised control problem addressed in this thesis, each platform and sensor system is considered to be a distinct decision maker with an individual information-theoretic utility measure capturing both local objectives and the inter-dependencies among the decisions made by other members of the team. Together these information-theoretic utilities constitute the team objective. The key contributions of this thesis lie in the quantification and study of cooperative control between sensors and platforms using information as a common utility measure. In particular, * The problem of information gathering is formulated as an optimal control problem by identifying formal measures of information with utility or pay-off. * An information-theoretic utility model of coupling and coordination between decentralised decision makers is elucidated. This is used to describe how the information gathering strategies of a team of autonomous systems are coupled. * Static and dynamic information structures for team members are defined. It is shown that the use of static information structures can lead to efficient, although sub-optimal, decentralised control strategies for the team. * Significant examples in decentralised control of a team of sensors are developed. These include the multi-vehicle multi-target bearings-only tracking problem, and the area coverage or exploration problem for multiple vehicles. These examples demonstrate the range of non-trivial problems to which the theory in this thesis can be employed

Present and Future of SLAM in Extreme Underground Environments

This paper reports on the state of the art in underground SLAM by discussing different SLAM strategies and results across six teams that participated in the three-year-long SubT competition. In particular, the paper has four main goals. First, we review the algorithms, architectures, and systems adopted by the teams; particular emphasis is put on lidar-centric SLAM solutions (the go-to approach for virtually all teams in the competition), heterogeneous multi-robot operation (including both aerial and ground robots), and real-world underground operation (from the presence of obscurants to the need to handle tight computational constraints). We do not shy away from discussing the dirty details behind the different SubT SLAM systems, which are often omitted from technical papers. Second, we discuss the maturity of the field by highlighting what is possible with the current SLAM systems and what we believe is within reach with some good systems engineering. Third, we outline what we believe are fundamental open problems, that are likely to require further research to break through. Finally, we provide a list of open-source SLAM implementations and datasets that have been produced during the SubT challenge and related efforts, and constitute a useful resource for researchers and practitioners.Comment: 21 pages including references. This survey paper is submitted to IEEE Transactions on Robotics for pre-approva

Topologies for combining the Internet of Things and Serious Games

Serious Games have been established over recent years as a means of utilising gaming for applications other than entertainment.With the emergence of the Internet of Things (IoT) paradigm, a new direction for serious games arises, where data gathered from the physical environment can be utilised towards new novel applications. This literature survey uncovers existing topologies that can be applied for combining IoT with Serious Games. This paper presents findings from extensive research into IoT, Serious Games, Pervasive Games and Gamification, IoT topologies and Wireless Sensor Networks (WSN), to identify the requirements of a topology for Serious Games and IoT. By understanding the topological requirements for combining IoT and Serious Games, the development process is reduced, allowing for the advancement in the mentioned field. Three topologies are presented for combining IoT with Serious Games and a detailed topology for developing a Serious Game that monitors student attendance is presented. Also included, is an insight into the new paradigm of Smart Serious Games (SSGs). This paper will aid future research and development in SSGs determine effective network topologies

Large-scale tree-based unfitted finite elements for metal additive manufacturing

This thesis addresses large-scale numerical simulations of partial differential equations posed on evolving geometries. Our target application is the simulation of metal additive manufacturing (or 3D printing) with powder-bed fusion methods, such as Selective Laser Melting (SLM), Direct Metal Laser Sintering (DMLS) or Electron-Beam Melting (EBM). The simulation of metal additive manufacturing processes is a remarkable computational challenge, because processes are characterised by multiple scales in space and time and multiple complex physics that occur in intricate three-dimensional growing-in-time geometries. Only the synergy of advanced numerical algorithms and high-performance scientific computing tools can fully resolve, in the short run, the simulation needs in the area. The main goal of this Thesis is to design a a novel highly-scalable numerical framework with multi-resolution capability in arbitrarily complex evolving geometries. To this end, the framework is built by combining three computational tools: (1) parallel mesh generation and adaptation with forest-of-trees meshes, (2) robust unfitted finite element methods and (3) parallel finite element modelling of the geometry evolution in time. Our numerical research is driven by several limitations and open questions in the state-of-the-art of the three aforementioned areas, which are vital to achieve our main objective. All our developments are deployed with high-end distributed-memory implementations in the large-scale open-source software project FEMPAR. In considering our target application, (4) temporal and spatial model reduction strategies for thermal finite element models are investigated. They are coupled to our new large-scale computational framework to simplify optimisation of the manufacturing process. The contributions of this Thesis span the four ingredients above. Current understanding of (1) is substantially improved with rigorous proofs of the computational benefits of the 2:1 k-balance (ease of parallel implementation and high-scalability) and the minimum requirements a parallel tree-based mesh must fulfil to yield correct parallel finite element solvers atop them. Concerning (2), a robust, optimal and scalable formulation of the aggregated unfitted finite element method is proposed on parallel tree-based meshes for elliptic problems with unfitted external contour or unfitted interfaces. To the author’s best knowledge, this marks the first time techniques (1) and (2) are brought together. After enhancing (1)+(2) with a novel parallel approach for (3), the resulting framework is able to mitigate a major performance bottleneck in large-scale simulations of metal additive manufacturing processes by powder-bed fusion: scalable adaptive (re)meshing in arbitrarily complex geometries that grow in time. Along the development of this Thesis, our application problem (4) is investigated in two joint collaborations with the Monash Centre for Additive Manufacturing and Monash University in Melbourne, Australia. The first contribution is an experimentally-supported thorough numerical assessment of time-lumping methods, the second one is a novel experimentally-validated formulation of a new physics-based thermal contact model, accounting for thermal inertia and suitable for model localisation, the so-called virtual domain approximation. By efficiently exploiting high-performance computing resources, our new computational framework enables large-scale finite element analysis of metal additive manufacturing processes, with increased fidelity of predictions and dramatical reductions of computing times. It can also be combined with the proposed model reductions for fast thermal optimisation of the manufacturing process. These tools open the path to accelerate the understanding of the process-to-performance link and digital product design and certification in metal additive manufacturing, two milestones that are vital to exploit the technology for mass-production.Aquesta tesi tracta la simulació a gran escala d'equacions en derivades parcials sobre geometries variables. L'aplicació principal és la simulació de procesos de fabricació additiva (o impressió 3D) amb metalls i per mètodes de fusió de llit de pols, com ara Selective Laser Melting (SLM), Direct Metal Laser Sintering (DMLS) o Electron-Beam Melting (EBM). La simulació d'aquests processos és un repte computacional excepcional, perquè els processos estan caracteritzats per múltiples escales espaitemporals i múltiples físiques que tenen lloc sobre geometries tridimensionals complicades que creixen en el temps. La sinèrgia entre algorismes numèrics avançats i eines de computació científica d'alt rendiment és la única via per resoldre completament i a curt termini les necessitats en simulació d'aquesta àrea. El principal objectiu d'aquesta tesi és dissenyar un nou marc numèric escalable de simulació amb capacitat de multiresolució en geometries complexes i variables. El nou marc es construeix unint tres eines computacionals: (1) mallat paral·lel i adaptatiu amb malles de boscs d'arbre, (2) mètodes d'elements finits immersos robustos i (3) modelització en paral·lel amb elements finits de geometries que creixen en el temps. Algunes limitacions i problemes oberts en l'estat de l'art, que són claus per aconseguir el nostre objectiu, guien la nostra recerca. Tots els desenvolupaments s'implementen en arquitectures de memòria distribuïda amb el programari d'accés obert FEMPAR. Quant al problema d'aplicació, (4) s'investiguen models reduïts en espai i temps per models tèrmics del procés. Aquests models reduïts s'acoplen al nostre marc computacional per simplificar l'optimització del procés. Les contribucions d'aquesta tesi abasten els quatre punts de dalt. L'estat de l'art de (1) es millora substancialment amb proves riguroses dels beneficis computacionals del 2:1 balancejat (fàcil paral·lelització i alta escalabilitat), així com dels requisits mínims que aquest tipus de mallat han de complir per garantir que els espais d'elements finits que s'hi defineixin estiguin ben posats. Quant a (2), s'ha formulat un mètode robust, òptim i escalable per agregació per problemes el·líptics amb contorn o interface immerses. Després d'augmentar (1)+(2) amb un nova estratègia paral·lela per (3), el marc de simulació resultant mitiga de manera efectiva el principal coll d'ampolla en la simulació de processos de fabricació additiva en llits de pols de metall: adaptivitat i remallat escalable en geometries complexes que creixen en el temps. Durant el desenvolupament de la tesi, es col·labora amb el Monash Centre for Additive Manufacturing i la Universitat de Monash de Melbourne, Austràlia, per investigar el problema d'aplicació. En primer lloc, es fa una anàlisi experimental i numèrica exhaustiva dels mètodes d'aggregació temporal. En segon lloc, es proposa i valida experimental una nova formulació de contacte tèrmic que té en compte la inèrcia tèrmica i és adequat per a localitzar el model, l'anomenada aproximació per dominis virtuals. Mitjançant l'ús eficient de recursos computacionals d'alt rendiment, el nostre nou marc computacional fa possible l'anàlisi d'elements finits a gran escala dels processos de fabricació additiva amb metalls, amb augment de la fidelitat de les prediccions i reduccions significatives de temps de computació. Així mateix, es pot combinar amb els models reduïts que es proposen per l'optimització tèrmica del procés de fabricació. Aquestes eines contribueixen a accelerar la comprensió del lligam procés-rendiment i la digitalització del disseny i certificació de productes en fabricació additiva per metalls, dues fites crucials per explotar la tecnologia en producció en massa.Postprint (published version

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Accurate Localization with Ultra-Wideband Ranging for Multi-Robot Systems

RÉSUMÉ : Avec l’avancement de la technologie matérielle et logicielle, l’application de l’automatisation et de la robotique se développe rapidement. Les systèmes multi-robots sont particulièrement prometteurs en raison de leur grande efficacité et robustesse. De tels systèmes peuvent être utilisés pour aider les humains à effectuer efficacement des tâches dangereuses ou pénibles, telles que l’intervention en cas de catastrophe, l’exploration souterraine, etc. Pour déployer un système multi-robot dans un environnement sans GPS, la coordination des robots dans le système est un défi crucial. Chaque robot doit avoir une estimation précise de sa propre position pour permettre aux robots du système de collaborer pour la réalisation de leur tâche. Comme cette direction de recherche est relativement nouvelle, les approches existantes ne sont pas encore abouties. Elles consistent principalement en des systèmes centralisés qui reposent sur des signaux GPS. La dépendance sur un signal GPS limite l’application aux espaces extérieurs ouverts. De plus, les systèmes centralisés sont confrontés au risque d’un point de défaillance unique, qui limite la robustesse du système. Par ailleurs, un système centralisé n’est pas toujours approprié à une taille grandissante, comme lors d’ajout de nouveaux groupes de robots ou lors de la fusion de différents groupes. Par conséquent, une solution distribuée, décentralisée, et adaptée à de larges groupes de tailles variables pouvant produire une estimation et un suivi du positionnement des robots dans un environnement sans GPS est souhaitée. Dans ce travail, nous adoptons une stratégie descendante pour relever ces défis.----------ABSTRACT : With the advancement of hardware and software technology, the everyday applications of automation and robotics are developing rapidly. Multi-robot systems are particularly promising because of their high efficiency and robustness. Such systems can be used to assist humans in performing dangerous or strenuous tasks, such as disaster response, subterranean exploration, etc. To deploy a multi-robot system in an environment without a global positioning system (GPS), coordinating the robots in the system is a crucial challenge. Each robot needs to have the correct tracking of its own and its teammates positions to enable the robots to cooperate. Because this research direction is relatively new, there are not many mature methods: existing approaches are mainly centralized systems that rely on GPS signals. The dependence on GPS restricts the application to the outdoors or indoor spaces with expensive infrastructure. Centralized systems also face the risk of a single point of failure, which is not acceptable for critical systems. In addition, centralized systems can be hard to scale both statically and dynamically (e.g. adding new groups of robots or merging different groups). Therefore, a distributed and scalable solution with accurate positioning and tracking in a GPS-denied environment is desired. In this work, we follow a top-down strategy to address these challenges

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

Anomaly Detection in Networks with Application to Financial Transaction Networks

This paper is motivated by the task of detecting anomalies in networks of financial transactions, with accounts as nodes and a directed weighted edge between two nodes denoting a money transfer. The weight of the edge is the transaction amount. Examples of anomalies in networks include long paths of large transaction amounts, rings of large payments, and cliques of accounts. There are many methods available which detect such specific structures in networks. Here we introduce a method which is able to detect previously unspecified anomalies in networks. The method is based on a combination of features from network comparison and spectral analysis as well as local statistics, yielding 140 main features. We then use a simple feature sum method, as well as a random forest method, in order to classify nodes as normal or anomalous. We test the method first on synthetic networks which we generated, and second on a set of synthetic networks which were generated without the methods team having access to the ground truth. The first set of synthetic networks was split in a training set of 70 percent of the networks, and a test set of 30 percent of the networks. The resulting classifier was then applied to the second set of synthetic networks. We compare our method with Oddball, a widely used method for anomaly detection in networks, as well as to random classification. While Oddball outperforms random classification, both our feature sum method and our random forest method outperform Oddball. On the test set, the random forest outperforms feature sum, whereas on the second synthetic data set, initially feature sum tends to pick up more anomalies than random forest, with this behaviour reversing for lower-scoring anomalies. In all cases, the top 2 percent of flagged anomalies contained on average over 90 percent of the planted anomalies