Intelligent embedded systems platform for vehicular cyber-physical systems

Intelligent vehicular cyber-physical systems (ICPSs) increase the reliability, efficiency and adaptability of urban mobility systems. Notably, ICPSs enable autonomous transportation in smart cities, exemplified by the emerging fields of self-driving cars and advanced air mobility. Nonetheless, the deployment of ICPSs raises legitimate concerns surrounding safety assurance, cybersecurity threats, communication reliability, and data management. Addressing these issues often necessitates specialised platforms to cater to the heterogeneity and complexity of ICPSs. To address this challenge, this paper presents a comprehensive CPS to explore, develop and test ICPSs and intelligent vehicular algorithms. A customisable embedded system is realised using a field programmable gate array, which is connected to a supervisory computer to enable networked operations and support advanced multi-agent algorithms. The platform remains compatible with multiple vehicular sensors, communication protocols and human–machine interfaces, essential for a vehicle to perceive its surroundings, communicate with collaborative systems, and interact with its occupants. The proposed CPS thereby offers a practical resource to advance ICPS development, comprehension, and experimentation in both educational and research settings. By bridging the gap between theory and practice, this tool empowers users to overcome the complexities of ICPSs and contribute to the emerging fields of autonomous transportation and intelligent vehicular systems

Comparison of state marginalization techniques in visual inertial navigation filters

The main focus of this thesis is finding and validating an efficient visual inertial navigation system (VINS) algorithm for applications in micro aerial vehicles (MAV). A typical VINS for a MAV consists of a low-cost micro electro mechanical system (MEMS) inertial measurement unit (IMU) and a monocular camera, which provides a minimum payload sensor setup. This setup is highly desirable for navigation of MAVs because highly resource constrains in the platform. However, bias and noise of lowcost IMUs demand sufficiently accurate VINS algorithms. Accurate VINS algorithms has been developed over the past decade but they demand higher computational resources. Therefore, resource limited MAVs demand computationally efficient VINS algorithms. This thesis considers the following computational cost elements in the VINS algorithm: feature tracking front-end, state marginalization technique and the complexity of the algorithm formulation. In this thesis three state-of-the-art feature tracking front ends were compared in terms of accuracy. (VINS-Mono front-end, MSCKF-Mono feature tracker and Matlab based feature tracker). Four state-ofthe- art state marginalization techniques (MSCKF-Generic marginalization, MSCKFMono marginalization, MSCKF-Two way marginalization and Two keyframe based epipolar constraint marginalization) were compared in terms of accuracy and efficiency. The complexity of the VINS algorithm formulation has also been compared using the filter execution time. The research study then presents the comparative analysis of the algorithms using a publicly available MAV benchmark datasets. Based on the results, an efficient VINS algorithm is proposed which is suitable for MAVs

Three dimensional surface reconstruction of lower limb prosthetic model using infrared sensor array

This thesis addresses the development of a shape detector device using infrared sensor to reconstruct a three-dimensional image of an object. The threedimension image is produced based on the object surface using image processing technique. Conventionally, infrared sensors are used for detection of an obstacle and distance measurement to avoid collisions. However, it is not common to use infrared sensors to measure the size of an object. Hence, this research aims to investigate the feasibility of infrared sensors in measuring the object dimension for three-dimension image reconstruction. Experiments were executed to study the minimum distance range utilising GP2D120 infrared sensor. From the experiment, the distance between the sensor and object surface should be more than 5 cm. The scanning device consists of the infrared sensor array was placed in a black box with the object in the center. The scanning process required the object to turn 360 ° clockwise in an xy plane and the resolution for z-axis is 2 mm, in order to obtain data for the image reconstruction. Reference polygon shape models with various dimensions were used as scanning objects in the experiments. The device scans object diameter every 2 mm in thickness, 100 mm in height, and the total time required to collect data for each layer is 60 seconds. The reconstructed object accuracy is above 80 % based on the comparison between a solid and printed model dimension. Four different lower limb prosthetic models with different shapes were used as the object in the scanning experiments. The experimental findings show that the prosthetic shapes reconstructed with an average accuracy of 97 %. This system shows good reproducibility where the collected data using the infrared sensor device need further improvement so that it can be applied in medical field for orthotics and prosthetics purpose

Optical metrology for digital manufacturing: a review

With the increasing adoption of Industry 4.0, optical metrology has experienced a significant boom in its implementation, as an ever-increasing number of manufacturing processes are overhauled for in-process measurement and control. As such, optical metrology for digital manufacturing is currently a hot topic in manufacturing research. Whilst contact coordinate measurement solutions have been adopted for many years, the current trend is to increasingly exploit the advantages given by optical measurement technologies. Smart automated non-contact inspection devices allow for faster cycle times, reducing the inspection time and having a continuous monitoring of process quality. In this paper a review for the state of the art in optical metrology is presented, highlighting the advantages and impacts of the integration of optical coordinate and surface texture measurement technologies in digital manufacturing processes. Also, the range of current software and hardware technologies for digital manufacturing metrology is discussed, as well as strategies for zero-defect manufacturing for greater sustainability, including examples and in-depth discussions of additive manufacturing applications. Finally, key current challenges are identified relating to measurement speed and data-processing bottlenecks; geometric complexity, part size and surface texture; user-dependent constraints, harsh environments and uncertainty evaluation

Study and Development of Mechatronic Devices and Machine Learning Schemes for Industrial Applications

Obiettivo del presente progetto di dottorato è lo studio e sviluppo di sistemi meccatronici e di modelli machine learning per macchine operatrici e celle robotizzate al fine di incrementarne le prestazioni operative e gestionali. Le pressanti esigenze del mercato hanno imposto lavorazioni con livelli di accuratezza sempre più elevati, tempi di risposta e di produzione ridotti e a costi contenuti. In questo contesto nasce il progetto di dottorato, focalizzato su applicazioni di lavorazioni meccaniche (e.g. fresatura), che includono sistemi complessi quali, ad esempio, macchine a 5 assi e, tipicamente, robot industriali, il cui utilizzo varia a seconda dell’impiego. Oltre alle specifiche problematiche delle lavorazioni, si deve anche considerare l’interazione macchina-robot per permettere un’efficiente capacità e gestione dell’intero impianto. La complessità di questo scenario può evidenziare sia specifiche problematiche inerenti alle lavorazioni (e.g. vibrazioni) sia inefficienze più generali che riguardano l’impianto produttivo (e.g. asservimento delle macchine con robot, consumo energetico). Vista la vastità della tematica, il progetto si è suddiviso in due parti, lo studio e sviluppo di due specifici dispositivi meccatronici, basati sull’impiego di attuatori piezoelettrici, che puntano principalmente alla compensazione di vibrazioni indotte dal processo di lavorazione, e l’integrazione di robot per l’asservimento di macchine utensili in celle robotizzate, impiegando modelli di machine learning per definire le traiettorie ed i punti di raggiungibilità del robot, al fine di migliorarne l’accuratezza del posizionamento del pezzo in diverse condizioni. In conclusione, la presente tesi vuole proporre soluzioni meccatroniche e di machine learning per incrementare le prestazioni di macchine e sistemi robotizzati convenzionali. I sistemi studiati possono essere integrati in celle robotizzate, focalizzandosi sia su problematiche specifiche delle lavorazioni in macchine operatrici sia su problematiche a livello di impianto robot-macchina. Le ricerche hanno riguardato un’approfondita valutazione dello stato dell’arte, la definizione dei modelli teorici, la progettazione funzionale e l’identificazione delle criticità del design dei prototipi, la realizzazione delle simulazioni e delle prove sperimentali e l’analisi dei risultati.The aim of this Ph.D. project is the study and development of mechatronic systems and machine learning models for machine tools and robotic applications to improve their performances. The industrial demands have imposed an ever-increasing accuracy and efficiency requirement whilst constraining the cost. In this context, this project focuses on machining processes (e.g. milling) that include complex systems such as 5-axes machine tool and industrial robots, employed for various applications. Beside the issues related to the machining process itself, the interaction between the machining centre and the robot must be considered for the complete industrial plant’s improvement. This scenario´s complexity depicts both specific machining problematics (e.g. vibrations) and more general issues related to the complete plant, such as machine tending with an industrial robot and energy consumption. Regarding the immensity of this area, this project is divided in two parts, the study and development of two mechatronic devices, based on piezoelectric stack actuators, for the active vibration control during the machining process, and the robot machine tending within the robotic cell, employing machine learning schemes for the trajectory definition and robot reachability to improve the corresponding positioning accuracy. In conclusion, this thesis aims to provide a set of solutions, based on mechatronic devices and machine learning schemes, to improve the conventional machining centre and the robotic systems performances. The studied systems can be integrated within a robotic cell, focusing on issues related to the specific machining process and to the interaction between robot-machining centre. This research required a thorough study of the state-of-the-art, the formulation of theoretical models, the functional design development, the identification of the critical aspects in the prototype designs, the simulation and experimental campaigns, and the analysis of the obtained results

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

Metodologia para calibração de robôs industriais com medições em sub-regiões volumétricas

Tese (doutorado) — Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2022.Este trabalho propõe uma metodologia para calibração de robôs industriais que utiliza o conceito de medição em sub-regiões, possibilitando soluções de baixo custo e de fácil implementação para atender os requisitos de precisão do robô em aplicações industriais. A implementação das soluções para aumentar a precisão dos robôs têm alto custo atualmente, tornando a calibração em todo o local de trabalho uma tarefa difícil e improvável na indústria. Assim, a redução do tempo gasto e do volume medido do espaço de trabalho com efetuador do robô são os principais benefícios da implementação do conceito de sub-região, garantindo flexibilidade suficiente na etapa de medição como parte dos procedimentos de calibração do manipulador. A principal contribuição deste trabalho é a proposta e discussão de uma metodologia para calibrar robôs utilizando vários pequenos volumes de medição e agrupar os dados medidos de forma equivalente às medições feitas em regiões de grande volume, viabilizando o uso de equipamentos de alta precisão, mas limitado a pequenos volumes, como sistemas de medição baseados em visão computacional. Os procedimentos de calibração do robô foram simulados de acordo com a literatura, de forma que os resultados da simulação estejam livres de erros devido a configurações experimentais para isolar os benefícios da metodologia de medição proposta. Procedimentos experimentais baseados na medição por sub-regiões foram realizados utilizando um Laser-Tracker, permitindo destacar os benefícios da medição em pequenos volumes. Além disso, é proposto um método para validar o modelo cinemático analítico off-line de robôs industriais utilizando o modelo nominal do fabricante do robô embarcado no seu controlador.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) e Fundação de Apoio à Pesquisa do Distrito Federal (FAP/DF).This work proposes a methodology for calibration of industrial robots that uses a concept of measurement sub-regions, allowing low-cost solutions and easy implementation to meet the robot accuracy requirements in industrial applications. The solutions to increasing the accuracy of robots today have highcost implementation, making calibration throughout the workplace in industry a difficult and unlikely task. Thus, reducing the time spent and the measured workspace volume of the robot end-effector are the main benefits of the implementation of the sub-region concept, ensuring sufficient flexibility in the measurement step of robot calibration procedures. The main contribution of this work is the proposal and discussion of a methodology to calibrate robots using several small measurement sub-regions and gathering the measurement data in a way equivalent to the measurements made in large volume regions, making feasible the use of high-precision measurement systems but limited to small volumes, such as vision-based measurement systems. The robot calibration procedures were simulated according to the literature, such that results from simulation are free from errors due to experimental setups as to isolate the benefits of the measurement proposal methodology. Experimental procedures based on the proposed methodology were performed using a Laser-Tracker, allowing to highlight the benefits of measurement in small regions. In addition, a method to validate the analytical off-line kinematic model of industrial robots is proposed using the nominal model of the robot supplier incorporated into its controller.Este trabajo propone una metodología para la calibración de robots industriales que utiliza el concepto de sub-regiones de medición, permitiendo que soluciones de bajo costo y fácil implementación cumplan con los requisitos de precisión del robot en aplicaciones industriales. Las soluciones para aumentar la precisión de los robots hoy en día se caracterizan por tener alto costo en la implementación, lo que hace que la calibración en todo el espacio de trabajo sea una tarea difícil e improbable en la industria. Por lo tanto, reducir el tiempo empleado y el volumen del espacio de trabajo medido del efector final del robot son los principales beneficios al implementar el concepto de sub-región, asegurando suficiente flexibilidad en la etapa de medición como parte de los procedimientos de calibración de robots. La principal contribución de este trabajo es la propuesta y discusión de una metodología para calibrar robots utilizando varias volúmenes pequeños de medición, recopilando los datos de medición de manera equivalente a las mediciones realizadas en regiones de gran volumen, haciendo factible el uso de sistemas de alta precisión, pero limitados a pequeños volúmenes, como los sistemas de medición basados en visión computacional. Los procedimientos de calibración del robot se simularon de acuerdo con la literatura, de manera que los resultados de la simulación estén libres de errores debido a las configuraciones experimentales para aislar los beneficios de la metodología de medición propuesta. Los procedimientos experimentales basados en la metodología mencionada se realizaron utilizando un Laser-Tracker, lo que permitió resaltar los beneficios de la medición en pequeñas regiones. Además, se propone un método para validar el modelo cinemático analítico off-line de robots industriales utilizando el modelo nominal del fabricante del robot incorporado en su controlador