The development of a human-robot interface for industrial collaborative system

Industrial robots have been identified as one of the most effective solutions for optimising output and quality within many industries. However, there are a number of manufacturing applications involving complex tasks and inconstant components which prohibit the use of fully automated solutions in the foreseeable future. A breakthrough in robotic technologies and changes in safety legislations have supported the creation of robots that coexist and assist humans in industrial applications. It has been broadly recognised that human-robot collaborative systems would be a realistic solution as an advanced production system with wide range of applications and high economic impact. This type of system can utilise the best of both worlds, where the robot can perform simple tasks that require high repeatability while the human performs tasks that require judgement and dexterity of the human hands. Robots in such system will operate as “intelligent assistants”. In a collaborative working environment, robot and human share the same working area, and interact with each other. This level of interface will require effective ways of communication and collaboration to avoid unwanted conflicts. This project aims to create a user interface for industrial collaborative robot system through integration of current robotic technologies. The robotic system is designed for seamless collaboration with a human in close proximity. The system is capable to communicate with the human via the exchange of gestures, as well as visual signal which operators can observe and comprehend at a glance. The main objective of this PhD is to develop a Human-Robot Interface (HRI) for communication with an industrial collaborative robot during collaboration in proximity. The system is developed in conjunction with a small scale collaborative robot system which has been integrated using off-the-shelf components. The system should be capable of receiving input from the human user via an intuitive method as well as indicating its status to the user ii effectively. The HRI will be developed using a combination of hardware integrations and software developments. The software and the control framework were developed in a way that is applicable to other industrial robots in the future. The developed gesture command system is demonstrated on a heavy duty industrial robot

Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems

Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, industrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision applications. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algorithms are interleaved pose a promising compromise between performance and generalizability. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) architectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

Exploitation of time-of-flight (ToF) cameras

This technical report reviews the state-of-the art in the field of ToF cameras, their advantages, their limitations, and their present-day applications sometimes in combination with other sensors. Even though ToF cameras provide neither higher resolution nor larger ambiguity-free range compared to other range map estimation systems, advantages such as registered depth and intensity data at a high frame rate, compact design, low weight and reduced power consumption have motivated their use in numerous areas of research. In robotics, these areas range from mobile robot navigation and map building to vision-based human motion capture and gesture recognition, showing particularly a great potential in object modeling and recognition.Preprin

Augmented reality in support of intelligent manufacturing – A systematic literature review

Industry increasingly moves towards digitally enabled ‘smart factories’ that utilise the internet of things (IoT) to realise intelligent manufacturing concepts like predictive maintenance or extensive machine to machine communication. A core technology to facilitate human integration in such a system is augmented reality (AR), which provides people with an interface to interact with the digital world of a smart factory. While AR is not ready yet for industrial deployment in some areas, it is already used in others. To provide an overview of research activities concerning AR in certain shop floor operations, a total of 96 relevant papers from 2011 to 2018 are reviewed. This paper presents the state of the art, the current challenges, and future directions of manufacturing related AR research through a systematic literature review and a citation network analysis. The results of this review indicate that the context of research concerning AR gets increasingly broader, especially by addressing challenges when implementing AR solutions.No funding was received

The cockpit for the 21st century

Interactive surfaces are a growing trend in many domains. As one possible manifestation of Mark Weiser’s vision of ubiquitous and disappearing computers in everywhere objects, we see touchsensitive screens in many kinds of devices, such as smartphones, tablet computers and interactive tabletops. More advanced concepts of these have been an active research topic for many years. This has also influenced automotive cockpit development: concept cars and recent market releases show integrated touchscreens, growing in size. To meet the increasing information and interaction needs, interactive surfaces offer context-dependent functionality in combination with a direct input paradigm. However, interfaces in the car need to be operable while driving. Distraction, especially visual distraction from the driving task, can lead to critical situations if the sum of attentional demand emerging from both primary and secondary task overextends the available resources. So far, a touchscreen requires a lot of visual attention since its flat surface does not provide any haptic feedback. There have been approaches to make direct touch interaction accessible while driving for simple tasks. Outside the automotive domain, for example in office environments, concepts for sophisticated handling of large displays have already been introduced. Moreover, technological advances lead to new characteristics for interactive surfaces by enabling arbitrary surface shapes. In cars, two main characteristics for upcoming interactive surfaces are largeness and shape. On the one hand, spatial extension is not only increasing through larger displays, but also by taking objects in the surrounding into account for interaction. On the other hand, the flatness inherent in current screens can be overcome by upcoming technologies, and interactive surfaces can therefore provide haptically distinguishable surfaces. This thesis describes the systematic exploration of large and shaped interactive surfaces and analyzes their potential for interaction while driving. Therefore, different prototypes for each characteristic have been developed and evaluated in test settings suitable for their maturity level. Those prototypes were used to obtain subjective user feedback and objective data, to investigate effects on driving and glance behavior as well as usability and user experience. As a contribution, this thesis provides an analysis of the development of interactive surfaces in the car. Two characteristics, largeness and shape, are identified that can improve the interaction compared to conventional touchscreens. The presented studies show that large interactive surfaces can provide new and improved ways of interaction both in driver-only and driver-passenger situations. Furthermore, studies indicate a positive effect on visual distraction when additional static haptic feedback is provided by shaped interactive surfaces. Overall, various, non-exclusively applicable, interaction concepts prove the potential of interactive surfaces for the use in automotive cockpits, which is expected to be beneficial also in further environments where visual attention needs to be focused on additional tasks.Der Einsatz von interaktiven Oberflächen weitet sich mehr und mehr auf die unterschiedlichsten Lebensbereiche aus. Damit sind sie eine mögliche Ausprägung von Mark Weisers Vision der allgegenwärtigen Computer, die aus unserer direkten Wahrnehmung verschwinden. Bei einer Vielzahl von technischen Geräten des täglichen Lebens, wie Smartphones, Tablets oder interaktiven Tischen, sind berührungsempfindliche Oberflächen bereits heute in Benutzung. Schon seit vielen Jahren arbeiten Forscher an einer Weiterentwicklung der Technik, um ihre Vorteile auch in anderen Bereichen, wie beispielsweise der Interaktion zwischen Mensch und Automobil, nutzbar zu machen. Und das mit Erfolg: Interaktive Benutzeroberflächen werden mittlerweile serienmäßig in vielen Fahrzeugen eingesetzt. Der Einbau von immer größeren, in das Cockpit integrierten Touchscreens in Konzeptfahrzeuge zeigt, dass sich diese Entwicklung weiter in vollem Gange befindet. Interaktive Oberflächen ermöglichen das flexible Anzeigen von kontextsensitiven Inhalten und machen eine direkte Interaktion mit den Bildschirminhalten möglich. Auf diese Weise erfüllen sie die sich wandelnden Informations- und Interaktionsbedürfnisse in besonderem Maße. Beim Einsatz von Bedienschnittstellen im Fahrzeug ist die gefahrlose Benutzbarkeit während der Fahrt von besonderer Bedeutung. Insbesondere visuelle Ablenkung von der Fahraufgabe kann zu kritischen Situationen führen, wenn Primär- und Sekundäraufgaben mehr als die insgesamt verfügbare Aufmerksamkeit des Fahrers beanspruchen. Herkömmliche Touchscreens stellen dem Fahrer bisher lediglich eine flache Oberfläche bereit, die keinerlei haptische Rückmeldung bietet, weshalb deren Bedienung besonders viel visuelle Aufmerksamkeit erfordert. Verschiedene Ansätze ermöglichen dem Fahrer, direkte Touchinteraktion für einfache Aufgaben während der Fahrt zu nutzen. Außerhalb der Automobilindustrie, zum Beispiel für Büroarbeitsplätze, wurden bereits verschiedene Konzepte für eine komplexere Bedienung großer Bildschirme vorgestellt. Darüber hinaus führt der technologische Fortschritt zu neuen möglichen Ausprägungen interaktiver Oberflächen und erlaubt, diese beliebig zu formen. Für die nächste Generation von interaktiven Oberflächen im Fahrzeug wird vor allem an der Modifikation der Kategorien Größe und Form gearbeitet. Die Bedienschnittstelle wird nicht nur durch größere Bildschirme erweitert, sondern auch dadurch, dass Objekte wie Dekorleisten in die Interaktion einbezogen werden können. Andererseits heben aktuelle Technologieentwicklungen die Restriktion auf flache Oberflächen auf, so dass Touchscreens künftig ertastbare Strukturen aufweisen können. Diese Dissertation beschreibt die systematische Untersuchung großer und nicht-flacher interaktiver Oberflächen und analysiert ihr Potential für die Interaktion während der Fahrt. Dazu wurden für jede Charakteristik verschiedene Prototypen entwickelt und in Testumgebungen entsprechend ihres Reifegrads evaluiert. Auf diese Weise konnten subjektives Nutzerfeedback und objektive Daten erhoben, und die Effekte auf Fahr- und Blickverhalten sowie Nutzbarkeit untersucht werden. Diese Dissertation leistet den Beitrag einer Analyse der Entwicklung von interaktiven Oberflächen im Automobilbereich. Weiterhin werden die Aspekte Größe und Form untersucht, um mit ihrer Hilfe die Interaktion im Vergleich zu herkömmlichen Touchscreens zu verbessern. Die durchgeführten Studien belegen, dass große Flächen neue und verbesserte Bedienmöglichkeiten bieten können. Außerdem zeigt sich ein positiver Effekt auf die visuelle Ablenkung, wenn zusätzliches statisches, haptisches Feedback durch nicht-flache Oberflächen bereitgestellt wird. Zusammenfassend zeigen verschiedene, untereinander kombinierbare Interaktionskonzepte das Potential interaktiver Oberflächen für den automotiven Einsatz. Zudem können die Ergebnisse auch in anderen Bereichen Anwendung finden, in denen visuelle Aufmerksamkeit für andere Aufgaben benötigt wird

Convolutional neural networks for hand gesture recognition with off-the-shelf radar sensor

openL'elaborato espone l'attività di ricerca riguardante l'applicazione di metodologie di Machine Learning per risolvere un problema di interazione uomo-macchina. L'obiettivo è riconoscere e classificare correttamente dei movimenti della mano eseguiti da un utente, i quali vengono catturati tramite un sensore radar. Il segnale viene successivamente processato e dato in input ad una rete neurale convoluzionale, seguita da un classificatore volto a riconoscere il movimento che viene eseguito.The thesis explains the research and metholodogies applied in order to solve a Human-Computer Interaction task by means of Machine Learning techniques. The goal is to recognize and classify hand gestures performed by the user, which are acquired with a radar sensor. The signal is then processed and given as input to a convolutional neural network, followed by a fully connected classifier that should be able to classify correctly the movement