Mobile Robot Path Planning in a Trajectory with Multiple Obstacles Using Genetic Algorithms

Path planning is an essential algorithm to help robots complete their task in the field quickly. However, some path planning algorithms are computationally expensive and cannot adapt to new environments with a distinctly different set of obstacles. This paper presents optimal path planning based on a genetic algorithm (GA) that is proposed to be carried out in a dynamic environment with various obstacles. First, the points of the feasible path are found by performing a local search procedure. Then, the points are optimized to find the shortest path. When the optimal path is calculated, the position of the points on the path is smoothed to avoid obstacles in the environment. Thus, the average fitness values and the GA generation are better than the traditional method. The simulation results show that the proposed algorithm successfully finds the optimal path in an environment with multiple obstacles. Compared to a traditional GA-based method, our proposed algorithm has a smoother route due to path optimization. Therefore, this makes the proposed method advantageous in a dynamic environment

Scalable Human-Machine Interaction System for Real-Time Care in the Internet of Health Things

The rise in numbers of individuals with weak immunity around the world and the aging of populations put an ever-growing pressure on healthcare and inevitably increases its cost. This phenomenon leads to larger portions of the population to which quality healthcare is not provided. To fight this trend, technological advancements in the Internet of Health Things aim to integrate smart sensors and devices to continuously monitor and assess the status of patients and older adults from the comfort of their own home at a fraction of the cost. Although solving specific problems each at a time advances the field and takes us a step closer to autonomous home care systems, the solution to these issues needs to consider the much larger picture to unify the approaches and cultivate benefits of many intelligent, but stand-alone, systems. The current work aims to explore the field of Internet of Health Things and its application to remote health monitoring and ambient assisted living for older adults. Picking up from where previous literature left off, this thesis proposes a multi-layered framework that provides a comprehensive solution to continuous healthcare. In particular, the framework was created with modularity, scalability, and expandability as the main priorities; to offer an all-purpose remedy to the problems in hand. To this end, the internal mechanisms of the framework are described in detail and the system is applied to remote health monitoring and ambient assisted living environments by interchanging its components. The implementations presented in this thesis expose the capability of the framework to harvest power of existing intelligent devices. Moreover, the two systems implemented consider multi-modal and natural human-machine interaction techniques that provide the user with the choice of their preferred interaction method. The main advantage of the proposed framework is that it offers an all-in-one solution to providing continuous healthcare without sacrificing the quality of care provided. On the contrary, the solution in this work allows deeper understanding of user's health, personalization, real-time analytics and recommendations, as well as aid for activities of daily living with state of the art technologies

Personenwiedererkennung mittels maschineller Lernverfahren für öffentliche Einsatzumgebungen

Die erscheinungsbasierte Personenwiedererkennung in öffentlichen Einsatzumgebungen ist eines der schwierigsten, noch ungelösten Probleme der Bildverarbeitung. Viele Teilprobleme können nur gelöst werden, wenn Methoden des maschinellen Lernens mit Methoden der Bildverarbeitung kombiniert werden. In dieser Arbeit werden maschinelle Lernverfahren eingesetzt, um alle Abarbeitungsschritte einer erscheinungsbasierten Personenwiedererkennung zu verbessern: Mithilfe von Convolutional Neural Networks werden erscheinungsbasierte Merkmale gelernt, die eine Wiedererkennung auf menschlichem Niveau ermöglichen. Für die Generierung des Templates zur Beschreibung der Zielperson wird durch Einsatz maschineller Lernverfahren eine automatische Auswahl personenspezifischer, diskriminativer Merkmale getroffen. Durch eine gelernte Metrik können beim Vergleich von Merkmalsvektoren szenariospezifische Umwelteinflüsse kompensiert werden. Eine Fusion komplementärer Merkmale auf Score Level steigert die Wiedererkennungsleistung deutlich. Dies wird vor allem durch eine gelernte Gewichtung der Merkmale erreicht. Das entwickelte Verfahren wird exemplarisch anhand zweier Einsatzszenarien - Videoüberwachung und Robotik - evaluiert. Bei der Videoüberwachung ermöglicht die Wiedererkennung von Personen ein kameraübergreifendes Tracking. Dies hilft menschlichen Operateuren, den Aufenthaltsort einer gesuchten Person in kurzer Zeit zu ermitteln. Durch einen mobilen Serviceroboter kann der aktuelle Nutzer anhand einer erscheinungsbasierten Wiedererkennung identifiziert werden. Dies hilft dem Roboter bei der Erfüllung von Aufgaben, bei denen er den Nutzer lotsen oder verfolgen muss. Die Qualität der erscheinungsbasierten Personenwiedererkennung wird in dieser Arbeit anhand von zwölf Kriterien charakterisiert, die einen Vergleich mit biometrischen Verfahren ermöglichen. Durch den Einsatz maschineller Lernverfahren wird bei der erscheinungsbasierten Personenwiedererkennung in den betrachteten unüberwachten, öffentlichen Einsatzfeldern eine Erkennungsleistung erzielt, die sich mit biometrischen Verfahren messen kann.Appearance-based person re-identification in public environments is one of the most challenging, still unsolved computer vision tasks. Many sub-tasks can only be solved by combining machine learning with computer vision methods. In this thesis, we use machine learning approaches in order to improve all processing steps of the appearance-based person re-identification: We apply convolutional neural networks for learning appearance-based features capable of performing re-identification at human level. For generating a template to describe the person of interest, we apply machine learning approaches that automatically select person-specific, discriminative features. A learned metric helps to compensate for scenario-specific perturbations while matching features. Fusing complementary features at score level improves the re-identification performance. This is achieved by a learned feature weighting. We deploy our approach in two applications, namely surveillance and robotics. In the surveillance application, person re-identification enables multi-camera tracking. This helps human operators to quickly determine the current location of the person of interest. By applying appearance-based re-identification, a mobile service robot is able to keep track of users when following or guiding them. In this thesis, we measure the quality of the appearance-based person re-identification by twelve criteria. These criteria enable a comparison with biometric approaches. Due to the application of machine learning techniques, in the considered unsupervised, public fields of application, the appearance-based person re-identification performs on par with biometric approaches.Die erscheinungsbasierte Personenwiedererkennung in öffentlichen Einsatzumgebungen ist eines der schwierigsten, noch ungelösten Probleme der Bildverarbeitung. Viele Teilprobleme können nur gelöst werden, wenn Methoden des maschinellen Lernens mit Methoden der Bildverarbeitung kombiniert werden. In dieser Arbeit werden maschinelle Lernverfahren eingesetzt, um alle Abarbeitungsschritte einer erscheinungsbasierten Personenwiedererkennung zu verbessern, sodass eine Wiedererkennung auf menschlichem Niveau ermöglicht wird. Das entwickelte Verfahren wird anhand zweier Einsatzszenarien — Videoüberwachung und Robotik — evaluiert. Bei der Videoüberwachung ermöglicht die Wiedererkennung von Personen ein kameraübergreifendes Tracking um den Aufenthaltsort einer gesuchten Person in kurzer Zeit zu ermitteln. Durch einen mobilen Serviceroboter kann der aktuelle Nutzer anhand einer erscheinungsbasierten Wiedererkennung identifiziert werden. Dies hilft dem Roboter beim Lots

Design e Ergonomia per la Human-Robot Interaction

This book investigates the relationship between design (specifically, the Human-Centred Design, Interaction Design and User Experience approaches) and the complex area of Human-Robot Interaction (specifically, social robotics for care). The research begins by framing the scientific problem of demographic aging and the increasing diffusion of wearable and robotic technologies for assisting and supporting the well-being and independence of the elderly and frail. Then, the research examines the role, contributions, and challenges of design in relation to the issue of acceptability in robotics, both from a theoretical-epistemological as well as from a practical-applicative viewpoint. The book, therefore, investigates methods and tools for implementing cross-disciplinary collaboration and for designing acceptability and interaction with new technologies in order to improve the quality of life and psychophysical health of human beings. The overall goal of the of the research presented in this volume is to bridge the gap between the two scientific areas of design and robotics, and let them converge in order to design assistive and social robots that can be effectively accepted as well as appropriate for people's specific needs. This is made possible through the development of a connection between the methodological approaches and tools of both disciplines in order to structure a framework for: cross-disciplinary collaboration and management of development processes in robotics research projects and design; and the application of the iterative process peculiar of HCD to robotics. On this basis, the research output was developed, namely the operational tool “Robotics & Design: the tool to design Human-Centered Assistive Robotics” which can be found at the link: www.roboticsdesign.org

Design e Ergonomia per la Human-Robot Interaction

This book investigates the relationship between design (specifically, the Human-Centred Design, Interaction Design and User Experience approaches) and the complex area of Human-Robot Interaction (specifically, social robotics for care). The research begins by framing the scientific problem of demographic aging and the increasing diffusion of wearable and robotic technologies for assisting and supporting the well-being and independence of the elderly and frail. Then, the research examines the role, contributions, and challenges of design in relation to the issue of acceptability in robotics, both from a theoretical-epistemological as well as from a practical-applicative viewpoint. The book, therefore, investigates methods and tools for implementing cross-disciplinary collaboration and for designing acceptability and interaction with new technologies in order to improve the quality of life and psychophysical health of human beings. The overall goal of the of the research presented in this volume is to bridge the gap between the two scientific areas of design and robotics, and let them converge in order to design assistive and social robots that can be effectively accepted as well as appropriate for people's specific needs. This is made possible through the development of a connection between the methodological approaches and tools of both disciplines in order to structure a framework for: cross-disciplinary collaboration and management of development processes in robotics research projects and design; and the application of the iterative process peculiar of HCD to robotics. On this basis, the research output was developed, namely the operational tool “Robotics & Design: the tool to design Human-Centered Assistive Robotics” which can be found at the link: www.roboticsdesign.org