Cooperative and Multimodal Capabilities Enhancement in the CERNTAURO Human–Robot Interface for Hazardous and Underwater Scenarios

The use of remote robotic systems for inspection and maintenance in hazardous environments is a priority for all tasks potentially dangerous for humans. However, currently available robotic systems lack that level of usability which would allow inexperienced operators to accomplish complex tasks. Moreover, the task’s complexity increases drastically when a single operator is required to control multiple remote agents (for example, when picking up and transporting big objects). In this paper, a system allowing an operator to prepare and configure cooperative behaviours for multiple remote agents is presented. The system is part of a human–robot interface that was designed at CERN, the European Center for Nuclear Research, to perform remote interventions in its particle accelerator complex, as part of the CERNTAURO project. In this paper, the modalities of interaction with the remote robots are presented in detail. The multimodal user interface enables the user to activate assisted cooperative behaviours according to a mission plan. The multi-robot interface has been validated at CERN in its Large Hadron Collider (LHC) mockup using a team of two mobile robotic platforms, each one equipped with a robotic manipulator. Moreover, great similarities were identified between the CERNTAURO and the TWINBOT projects, which aim to create usable robotic systems for underwater manipulations. Therefore, the cooperative behaviours were validated within a multi-robot pipe transport scenario in a simulated underwater environment, experimenting more advanced vision techniques. The cooperative teleoperation can be coupled with additional assisted tools such as vision-based tracking and grasping determination of metallic objects, and communication protocols design. The results show that the cooperative behaviours enable a single user to face a robotic intervention with more than one robot in a safer way

Wireless Sensor Networks for Underwater Localization: A Survey

Autonomous Underwater Vehicles (AUVs) have widely deployed in marine investigation and ocean exploration in recent years. As the fundamental information, their position information is not only for data validity but also for many real-world applications. Therefore, it is critical for the AUV to have the underwater localization capability. This report is mainly devoted to outline the recent advance- ment of Wireless Sensor Networks (WSN) based underwater localization. Several classic architectures designed for Underwater Acoustic Sensor Network (UASN) are brie y introduced. Acoustic propa- gation and channel models are described and several ranging techniques are then explained. Many state-of-the-art underwater localization algorithms are introduced, followed by the outline of some existing underwater localization systems

Evolutionary Robot Swarms Under Real-World Constraints

Tese de doutoramento em Engenharia Electrotécnica e de Computadores, na especialidade de Automação e Robótica, apresentada ao Departamento de Engenharia Electrotécnica e de Computadores da Faculdade de Ciências e Tecnologia da Universidade de CoimbraNas últimas décadas, vários cientistas e engenheiros têm vindo a estudar as estratégias provenientes da natureza. Dentro das arquiteturas biológicas, as sociedades que vivem em enxames revelam que agentes simplistas, tais como formigas ou pássaros, são capazes de realizar tarefas complexas usufruindo de mecanismos de cooperação. Estes sistemas abrangem todas as condições necessárias para a sobrevivência, incorporando comportamentos de cooperação, competição e adaptação. Na “batalha” sem fim em prol do progresso dos mecanismos artificiais desenvolvidos pelo homem, a ciência conseguiu simular o primeiro comportamento em enxame no final dos anos oitenta. Desde então, muitas outras áreas, entre as quais a robótica, beneficiaram de mecanismos de tolerância a falhas inerentes da inteligência coletiva de enxames. A área de investigação deste estudo incide na robótica de enxame, consistindo num domínio particular dos sistemas robóticos cooperativos que incorpora os mecanismos de inteligência coletiva de enxames na robótica. Mais especificamente, propõe-se uma solução completa de robótica de enxames a ser aplicada em contexto real. Nesta ótica, as operações de busca e salvamento foram consideradas como o caso de estudo principal devido ao nível de complexidade associado às mesmas. Tais operações ocorrem tipicamente em cenários dinâmicos de elevadas dimensões, com condições adversas que colocam em causa a aplicabilidade dos sistemas robóticos cooperativos. Este estudo centra-se nestes problemas, procurando novos desafios que não podem ser ultrapassados através da simples adaptação da literatura da especialidade em algoritmos de enxame, planeamento, controlo e técnicas de tomada de decisão. As contribuições deste trabalho sustentam-se em torno da extensão do método Particle Swarm Optimization (PSO) aplicado a sistemas robóticos cooperativos, denominado de Robotic Darwinian Particle Swarm Optimization (RDPSO). O RDPSO consiste numa arquitetura robótica de enxame distribuída que beneficia do particionamento dinâmico da população de robôs utilizando mecanismos evolucionários de exclusão social baseados na sobrevivência do mais forte de Darwin. No entanto, apesar de estar assente no caso de estudo do RDPSO, a aplicabilidade dos conceitos aqui propostos não se encontra restrita ao mesmo, visto que todos os algoritmos parametrizáveis de enxame de robôs podem beneficiar de uma abordagem idêntica. Os fundamentos em torno do RDPSO são introduzidos, focando-se na dinâmica dos robôs, nos constrangimentos introduzidos pelos obstáculos e pela comunicação, e nas suas propriedades evolucionárias. Considerando a colocação inicial dos robôs no ambiente como algo fundamental para aplicar sistemas de enxames em aplicações reais, é assim introduzida uma estratégia de colocação de robôs realista. Para tal, a população de robôs é dividida de forma hierárquica, em que são utilizadas plataformas mais robustas para colocar as plataformas de enxame no cenário de forma autónoma. Após a colocação dos robôs no cenário, é apresentada uma estratégia para permitir a criação e manutenção de uma rede de comunicação móvel ad hoc com tolerância a falhas. Esta estratégia não considera somente a distância entre robôs, mas também a qualidade do nível de sinal rádio frequência, redefinindo assim a sua aplicabilidade em cenários reais. Os aspetos anteriormente mencionados estão sujeitos a uma análise detalhada do sistema de comunicação inerente ao algoritmo, para atingir uma implementação mais escalável do RDPSO a cenários de elevada complexidade. Esta elevada complexidade inerente à dinâmica dos cenários motivaram a ultimar o desenvolvimento do RDPSO, integrando para o efeito um mecanismo adaptativo baseado em informação contextual (e.g., nível de atividade do grupo). Face a estas considerações, o presente estudo pode contribuir para expandir o estado-da-arte em robótica de enxame com algoritmos inovadores aplicados em contexto real. Neste sentido, todos os métodos propostos foram extensivamente validados e comparados com alternativas, tanto em simulação como com robôs reais. Para além disso, e dadas as limitações destes (e.g., número limitado de robôs, cenários de dimensões limitadas, constrangimentos reais limitados), este trabalho contribui ainda para um maior aprofundamento do estado-da-arte, onde se propõe um modelo macroscópico capaz de capturar a dinâmica inerente ao RDPSO e, até certo ponto, estimar analiticamente o desempenho coletivo dos robôs perante determinada tarefa. Em suma, esta investigação pode ter aplicabilidade prática ao colmatar a lacuna que se faz sentir no âmbito das estratégias de enxames de robôs em contexto real e, em particular, em cenários de busca e salvamento.Over the past decades, many scientists and engineers have been studying nature’s best and time-tested patterns and strategies. Within the existing biological architectures, swarm societies revealed that relatively unsophisticated agents with limited capabilities, such as ants or birds, were able to cooperatively accomplish complex tasks necessary for their survival. Those simplistic systems embrace all the conditions necessary to survive, thus embodying cooperative, competitive and adaptive behaviours. In the never-ending battle to advance artificial manmade mechanisms, computer scientists simulated the first swarm behaviour designed to mimic the flocking behaviour of birds in the late eighties. Ever since, many other fields, such as robotics, have benefited from the fault-tolerant mechanism inherent to swarm intelligence. The area of research presented in this Ph.D. Thesis focuses on swarm robotics, which is a particular domain of multi-robot systems (MRS) that embodies the mechanisms of swarm intelligence into robotics. More specifically, this Thesis proposes a complete swarm robotic solution that can be applied to real-world missions. Although the proposed methods do not depend on any particular application, search and rescue (SaR) operations were considered as the main case study due to their inherent level of complexity. Such operations often occur in highly dynamic and large scenarios, with harsh and faulty conditions, that pose several problems to MRS applicability. This Thesis focuses on these problems raising new challenges that cannot be handled appropriately by simple adaptation of state-of-the-art swarm algorithms, planning, control and decision-making techniques. The contributions of this Thesis revolve around an extension of the Particle Swarm Optimization (PSO) to MRS, denoted as Robotic Darwinian Particle Swarm Optimization (RDPSO). The RDPSO is a distributed swarm robotic architecture that benefits from the dynamical partitioning of the whole swarm of robots by means of an evolutionary social exclusion mechanism based on Darwin’s survival-of-the-fittest. Nevertheless, although currently applied solely to the RDPSO case study, the applicability of all concepts herein proposed is not restricted to it, since all parameterized swarm robotic algorithms may benefit from a similar approach The RDPSO is then proposed and used to devise the applicability of novel approaches. The fundamentals around the RDPSO are introduced by focusing on robots’ dynamics, obstacle avoidance, communication constraints and its evolutionary properties. Afterwards, taking the initial deployment of robots within the environment as a basis for applying swarm robotics systems into real-world applications, the development of a realistic deployment strategy is proposed. For that end, the population of robots is hierarchically divided, wherein larger support platforms autonomously deploy smaller exploring platforms in the scenario, while considering communication constraints and obstacles. After the deployment, a way of ensuring a fault-tolerant multi-hop mobile ad hoc communication network (MANET) is introduced to explicitly exchange information needed in a collaborative realworld task execution. Such strategy not only considers the maximum communication range between robots, but also the minimum signal quality, thus refining the applicability to real-world context. This is naturally followed by a deep analysis of the RDPSO communication system, describing the dynamics of the communication data packet structure shared between teammates. Such procedure is a first step to achieving a more scalable implementation by optimizing the communication procedure between robots. The highly dynamic characteristics of real-world applications motivated us to ultimate the RDPSO development with an adaptive strategy based on a set of context-based evaluation metrics. This thesis contributes to the state-of-the-art in swarm robotics with novel algorithms for realworld applications. All of the proposed approaches have been extensively validated in benchmarking tasks, in simulation, and with real robots. On top of that, and due to the limitations inherent to those (e.g., number of robots, scenario dimensions, real-world constraints), this Thesis further contributes to the state-of-the-art by proposing a macroscopic model able to capture the RDPSO dynamics and, to some extent, analytically estimate the collective performance of robots under a certain task. It is the author’s expectation that this Ph.D. Thesis may shed some light into bridging the reality gap inherent to the applicability of swarm strategies to real-world scenarios, and in particular to SaR operations.FCT - SFRH/BD /73382/201

Sensor Fusion for Mobile Robot Localization using UWB and ArUco Markers

Uma das principais características para considerar um robô autónomo é o facto de este ser capaz de se localizar, em tempo real, no seu ambiente, ou seja saber a sua posição e orientação. Esta é uma área desafiante que tem sido estudada por diversos investigadores em todo o mundo. Para obter a localização de um robô é possível recorrer a diferentes metodologias. No entanto há metodologias que apresentam problemas em diferentes circunstâncias, como é o caso da odometria que sofre de acumulação de erros com a distância percorrida pelo robô. Outro problema existente em diversas metodologias é a incerteza na deteção do robô devido a ruído presente nos sensores. Com o intuito de obter uma localização mais robusta do robô e mais tolerante a falhas é possível combinar diversos sistemas de localização, combinando assim as vantagens de cada um deles. Neste trabalho, será utilizado o sistema Pozyx, uma solução de baixo custo que fornece informação de posicionamento com o auxílio da tecnologia Ultra-WideBand Time-of-Flight (UWB ToF). Também serão utilizados marcadores ArUco colocados no ambiente que através da sua identificação por uma câmara é também possível obter informação de posicionamento. Estas duas soluções irão ser estudadas e implementadas num robô móvel, através de um esquema de localização baseada em marcadores. Primeiramente, irá ser feita uma caracterização do erro de ambos os sistemas, uma vez que as medidas não são perfeitas, havendo sempre algum ruído nas medições. De seguida, as medidas fornecidas pelos sistemas irão ser filtradas e fundidas com os valores da odometria do robô através da implementação de um Filtro de Kalman Extendido (EKF). Assim, é possível obter a pose do robô (posição e orientação), pose esta que é comparada com a pose fornecida por um sistema de Ground-Truth igualmente desenvolvido para este trabalho com o auxílio da libraria ArUco, percebendo assim a precisão do algoritmo desenvolvido. O trabalho desenvolvido mostrou que com a utilização do sistema Pozyx e dos marcadores ArUco é possível melhorar a localização do robô, o que significa que é uma solução adequada e eficaz para este fim.One of the main characteristics to consider a robot truly autonomous is the fact that it is able to locate itself, in real time, in its environment, that is, to know its position and orientation. This is a challenging area that has been studied by several researchers around the world. To obtain the localization of a robot it is possible to use different methodologies. However, there are methodologies that present problems in different circumstances, as is the case of odometry that suffers from error accumulation with the distance traveled by the robot. Another problem existing in several methodologies is the uncertainty in the sensing of the robot due to noise present in the sensors. In order to obtain a more robust localization of the robot and more fault tolerant it is possible to combine several localization systems, thus combining the advantages of each one. In this work, the Pozyx system will be used, a low-cost solution that provides positioning information through Ultra-WideBand Time-of-Flight (UWB ToF) technology. It will also be used ArUco markers placed in the environment that through their identification by a camera it is also possible to obtain positioning information. These two solutions will be studied and implemented in a mobile robot, through a beacon-based localization scheme. First, an error characterization of both systems will be performed, since the measurements are not perfect, and there is always some noise in the measurements. Next, the measurements provided by the systems will be filtered and fused with the robot's odometry values by the implementation of an Extended Kalman Filter (EKF). In this way, it is possible to obtain the robot's pose, i.e position and orientation, which is compared with the pose provided by a Ground-Truth system also developed for this work with the aid of the ArUco library, thus realizing the accuracy of the developed algorithm. The developed work showed that with the use of the Pozyx system and ArUco markers it is possible to improve the robot localization, meaning that it is an adequate and effective solution for this purpose

Mapping and Semantic Perception for Service Robotics

Para realizar una tarea, los robots deben ser capaces de ubicarse en el entorno. Si un robot no sabe dónde se encuentra, es imposible que sea capaz de desplazarse para alcanzar el objetivo de su tarea. La localización y construcción de mapas simultánea, llamado SLAM, es un problema estudiado en la literatura que ofrece una solución a este problema. El objetivo de esta tesis es desarrollar técnicas que permitan a un robot comprender el entorno mediante la incorporación de información semántica. Esta información también proporcionará una mejora en la localización y navegación de las plataformas robóticas. Además, también demostramos cómo un robot con capacidades limitadas puede construir de forma fiable y eficiente los mapas semánticos necesarios para realizar sus tareas cotidianas.El sistema de construcción de mapas presentado tiene las siguientes características: En el lado de la construcción de mapas proponemos la externalización de cálculos costosos a un servidor en nube. Además, proponemos métodos para registrar información semántica relevante con respecto a los mapas geométricos estimados. En cuanto a la reutilización de los mapas construidos, proponemos un método que combina la construcción de mapas con la navegación de un robot para explorar mejor un entorno y disponer de un mapa semántico con los objetos relevantes para una misión determinada.En primer lugar, desarrollamos un algoritmo semántico de SLAM visual que se fusiona los puntos estimados en el mapa, carentes de sentido, con objetos conocidos. Utilizamos un sistema monocular de SLAM basado en un EKF (Filtro Extendido de Kalman) centrado principalmente en la construcción de mapas geométricos compuestos únicamente por puntos o bordes; pero sin ningún significado o contenido semántico asociado. El mapa no anotado se construye utilizando sólo la información extraída de una secuencia de imágenes monoculares. La parte semántica o anotada del mapa -los objetos- se estiman utilizando la información de la secuencia de imágenes y los modelos de objetos precalculados. Como segundo paso, mejoramos el método de SLAM presentado anteriormente mediante el diseño y la implementación de un método distribuido. La optimización de mapas y el almacenamiento se realiza como un servicio en la nube, mientras que el cliente con poca necesidad de computo, se ejecuta en un equipo local ubicado en el robot y realiza el cálculo de la trayectoria de la cámara. Los ordenadores con los que está equipado el robot se liberan de la mayor parte de los cálculos y el único requisito adicional es una conexión a Internet.El siguiente paso es explotar la información semántica que somos capaces de generar para ver cómo mejorar la navegación de un robot. La contribución en esta tesis se centra en la detección 3D y en el diseño e implementación de un sistema de construcción de mapas semántico.A continuación, diseñamos e implementamos un sistema de SLAM visual capaz de funcionar con robustez en entornos poblados debido a que los robots de servicio trabajan en espacios compartidos con personas. El sistema presentado es capaz de enmascarar las zonas de imagen ocupadas por las personas, lo que aumenta la robustez, la reubicación, la precisión y la reutilización del mapa geométrico. Además, calcula la trayectoria completa de cada persona detectada con respecto al mapa global de la escena, independientemente de la ubicación de la cámara cuando la persona fue detectada.Por último, centramos nuestra investigación en aplicaciones de rescate y seguridad. Desplegamos un equipo de robots en entornos que plantean múltiples retos que implican la planificación de tareas, la planificación del movimiento, la localización y construcción de mapas, la navegación segura, la coordinación y las comunicaciones entre todos los robots. La arquitectura propuesta integra todas las funcionalidades mencionadas, asi como varios aspectos de investigación novedosos para lograr una exploración real, como son: localización basada en características semánticas-topológicas, planificación de despliegue en términos de las características semánticas aprendidas y reconocidas, y construcción de mapas.In order to perform a task, robots need to be able to locate themselves in the environment. If a robot does not know where it is, it is impossible for it to move, reach its goal and complete the task. Simultaneous Localization and Mapping, known as SLAM, is a problem extensively studied in the literature for enabling robots to locate themselves in unknown environments. The goal of this thesis is to develop and describe techniques to allow a service robot to understand the environment by incorporating semantic information. This information will also provide an improvement in the localization and navigation of robotic platforms. In addition, we also demonstrate how a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. The mapping system as built has the following features. On the map building side we propose the externalization of expensive computations to a cloud server. Additionally, we propose methods to register relevant semantic information with respect to the estimated geometrical maps. Regarding the reuse of the maps built, we propose a method that combines map building with robot navigation to better explore a room in order to obtain a semantic map with the relevant objects for a given mission. Firstly, we develop a semantic Visual SLAM algorithm that merges traditional with known objects in the estimated map. We use a monocular EKF (Extended Kalman Filter) SLAM system that has mainly been focused on producing geometric maps composed simply of points or edges but without any associated meaning or semantic content. The non-annotated map is built using only the information extracted from an image sequence. The semantic or annotated parts of the map –the objects– are estimated using the information in the image sequence and the precomputed object models. As a second step we improve the EKF SLAM presented previously by designing and implementing a visual SLAM system based on a distributed framework. The expensive map optimization and storage is allocated as a service in the Cloud, while a light camera tracking client runs on a local computer. The robot’s onboard computers are freed from most of the computation, the only extra requirement being an internet connection. The next step is to exploit the semantic information that we are able to generate to see how to improve the navigation of a robot. The contribution of this thesis is focused on 3D sensing which we use to design and implement a semantic mapping system. We then design and implement a visual SLAM system able to perform robustly in populated environments due to service robots work in environments where people are present. The system is able to mask the image regions occupied by people out of the rigid SLAM pipeline, which boosts the robustness, the relocation, the accuracy and the reusability of the geometrical map. In addition, it estimates the full trajectory of each detected person with respect to the scene global map, irrespective of the location of the moving camera at the point when the people were imaged. Finally, we focus our research on rescue and security applications. The deployment of a multirobot team in confined environments poses multiple challenges that involve task planning, motion planning, localization and mapping, safe navigation, coordination and communications among all the robots. The architecture integrates, jointly with all the above-mentioned functionalities, several novel features to achieve real exploration: localization based on semantic-topological features, deployment planning in terms of the semantic features learned and recognized, and map building.<br /

FracBot: Design of wireless underground sensor networks for mapping hydraulic fractures and determining reservoir parameters in unconventional systems

Wireless underground sensor networks (WUSNs) enable a wide variety of emerging applications that are not possible with current underground monitoring techniques, which require miniaturized wireless sensor systems for mapping hydraulic fractures, monitoring unconventional reservoirs and measuring other wellbore parameters. We call these devices FracBots (Fracture Robots), an extension of RFID (Radio Frequency IDentifcation) tags that realize WUSNs for mapping and characterization of hydraulic fractures in unconventional reservoirs. The objective of this thesis is to design fully integrated magnetic induction (MI)-based FracBots (WUSNs) that enable reliable and e fficient wireless communications in underground oil reservoirs for performing the in-situ monitoring of oil reservoirs. This is very crucial for determining the sweet spot of oil and natural gas reserves. To this end, we have contributed in four areas as follows: fi rst, we develop a novel cross-layer communication framework for MI-based FracBot networks in dynamically changing underground environments. The framework combines a joint selection of modulation, channel coding, power control and a geographic forwarding paradigm. Second, we develop a novel MI-based localization framework that exploits the unique properties of MI- eld to determine the locations of the randomly deployed FracBot nodes in oil reservoirs. Third, we develop an accurate energy framework of a linear FracBot network topology that generates feasible nodes' transmission rates and network topology while always guaranteeing su fficient energy. Then, we design, develop, and fabricate MI-based FracBot nodes. Finally, to validate the performance of our solutions in our produced prototype of FracBot nodes, we develop a physical MI-based WUSN testbed.Ph.D

Models for Efficient Automated Site Data Acquisition

Accurate and timely data acquisition for tracking and progress reporting is essential for efficient management and successful project delivery. Considerable research work has been conducted to develop methods utilizing automated site data acquisition for tracking and progress reporting. However, these developments are challenged by: the dynamic and noisy nature of construction jobsites; the indoor localization accuracy; and the data processing and extraction of actionable information. Limited research work attempted to study and develop customized design of wireless sensor networks to meet the above challenges and overcome limitations of utilizing off-the-shelf technologies. The objective of this research is to study, design, configure and develop fully customized automated site data acquisition models, with a special focus on near real-time automated tracking and control of construction operations embracing cutting edge innovations in wireless and remote sensing technologies. In this context, wireless and remote sensing technologies are integrated in two customized prototypes to monitor and collect data from construction jobsites. This data is then processed and mined to generate meaningful and actionable information. The developed prototypes are expected to have wider scope of applications in construction management, such as improving construction safety, monitoring the condition of civil infrastructure and reducing energy consumption in buildings. Two families of prototypes were developed in this research; Sensor Aided GPS (SA-GPS) prototype, which is designed and developed for tracking outdoor construction operations such as earthmoving; and Self-Calibrated Wireless Sensor Network (SC-WSN), which is designed for indoor localization and tracking of construction resources (labor, materials and equipment). These prototypes along with their hardware and software are encapsulated in a computational framework. The framework houses a set of algorithms coded in C# to enable efficient data processing and fusion that support tracking and progress reporting. Both the hardware prototypes and software algorithms were progressively tested, evaluated and re-designed using Rapid Prototyping approach. The validation process of the developed prototypes encompasses three steps; (1) simulation to validate the prototypes’ design virtually using MATLAB, (2) laboratory experiments to evaluate prototypes’ functionality in real time, and (3) testing on scaled case studies after fine-tuning the prototype design based on the results obtained from the first two steps. The SA-GPS prototype consists of a microcontroller equipped with GPS module as well as a number of sensors such as accelerometer, barometric pressure sensor, Bluetooth proximity and strain gauges. The results of testing the developed SA-GPS prototype on scaled construction jobsite indicated that it was capable of estimating project progress within 3% mean absolute percentage error and 1% standard deviation on 16 trials, in comparison to the standalone GPS which had approximately 12% mean absolute percentage error and 2% standard deviation. The SC-WSN prototype incorporates two main features. The first is the use of the Kalman filtering and smoothing for the RSSI signal to provide more stable and predictable signal for estimating the distance between a reader and a tag. The second is the use of a developed dynamic path-loss model which continually optimizes its parameters to cope with the dynamically changing construction environment using Particle Swarm Optimization (PSO) algorithm. The laboratory testing indicated the improvement in location estimation, where the produced location estimates using SC_WSN had an average error of 0.66m in comparison to 1.67m using the raw RSSI signal. Also the results indicated 60% accuracy improvement in estimating locations using the developed dynamic model. The developed prototypes are not only expected to reduce the risk of project cost and duration overruns by timely and early detection of deviations from project plan, but also enables project managers to observe and oversee their project’s status in near real-time. It is expected that the accuracy of the developed hardware, can be achieved on large-scale real construction projects. This is attributed to the fact that the developed prototype does not require any scalable improvements on its hardware technology, nor does it require any additional computational changes to its developed algorithms and software

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation