Nonlinear Modeling and Control of Driving Interfaces and Continuum Robots for System Performance Gains

With the rise of (semi)autonomous vehicles and continuum robotics technology and applications, there has been an increasing interest in controller and haptic interface designs. The presence of nonlinearities in the vehicle dynamics is the main challenge in the selection of control algorithms for real-time regulation and tracking of (semi)autonomous vehicles. Moreover, control of continuum structures with infinite dimensions proves to be difficult due to their complex dynamics plus the soft and flexible nature of the manipulator body. The trajectory tracking and control of automobile and robotic systems requires control algorithms that can effectively deal with the nonlinearities of the system without the need for approximation, modeling uncertainties, and input disturbances. Control strategies based on a linearized model are often inadequate in meeting precise performance requirements. To cope with these challenges, one must consider nonlinear techniques. Nonlinear control systems provide tools and methodologies for enabling the design and realization of (semi)autonomous vehicle and continuum robots with extended specifications based on the operational mission profiles. This dissertation provides an insight into various nonlinear controllers developed for (semi)autonomous vehicles and continuum robots as a guideline for future applications in the automobile and soft robotics field. A comprehensive assessment of the approaches and control strategies, as well as insight into the future areas of research in this field, are presented.First, two vehicle haptic interfaces, including a robotic grip and a joystick, both of which are accompanied by nonlinear sliding mode control, have been developed and studied on a steer-by-wire platform integrated with a virtual reality driving environment. An operator-in-the-loop evaluation that included 30 human test subjects was used to investigate these haptic steering interfaces over a prescribed series of driving maneuvers through real time data logging and post-test questionnaires. A conventional steering wheel with a robust sliding mode controller was used for all the driving events for comparison. Test subjects operated these interfaces for a given track comprised of a double lane-change maneuver and a country road driving event. Subjective and objective results demonstrate that the driver’s experience can be enhanced up to 75.3% with a robotic steering input when compared to the traditional steering wheel during extreme maneuvers such as high-speed driving and sharp turn (e.g., hairpin turn) passing. Second, a cellphone-inspired portable human-machine-interface (HMI) that incorporated the directional control of the vehicle as well as the brake and throttle functionality into a single holistic device will be presented. A nonlinear adaptive control technique and an optimal control approach based on driver intent were also proposed to accompany the mechatronic system for combined longitudinal and lateral vehicle guidance. Assisting the disabled drivers by excluding extensive arm and leg movements ergonomically, the device has been tested in a driving simulator platform. Human test subjects evaluated the mechatronic system with various control configurations through obstacle avoidance and city road driving test, and a conventional set of steering wheel and pedals were also utilized for comparison. Subjective and objective results from the tests demonstrate that the mobile driving interface with the proposed control scheme can enhance the driver’s performance by up to 55.8% when compared to the traditional driving system during aggressive maneuvers. The system’s superior performance during certain vehicle maneuvers and approval received from the participants demonstrated its potential as an alternative driving adaptation for disabled drivers. Third, a novel strategy is designed for trajectory control of a multi-section continuum robot in three-dimensional space to achieve accurate orientation, curvature, and section length tracking. The formulation connects the continuum manipulator dynamic behavior to a virtual discrete-jointed robot whose degrees of freedom are directly mapped to those of a continuum robot section under the hypothesis of constant curvature. Based on this connection, a computed torque control architecture is developed for the virtual robot, for which inverse kinematics and dynamic equations are constructed and exploited, with appropriate transformations developed for implementation on the continuum robot. The control algorithm is validated in a realistic simulation and implemented on a six degree-of-freedom two-section OctArm continuum manipulator. Both simulation and experimental results show that the proposed method could manage simultaneous extension/contraction, bending, and torsion actions on multi-section continuum robots with decent tracking performance (e.g. steady state arc length and curvature tracking error of 3.3mm and 130mm-1, respectively). Last, semi-autonomous vehicles equipped with assistive control systems may experience degraded lateral behaviors when aggressive driver steering commands compete with high levels of autonomy. This challenge can be mitigated with effective operator intent recognition, which can configure automated systems in context-specific situations where the driver intends to perform a steering maneuver. In this article, an ensemble learning-based driver intent recognition strategy has been developed. A nonlinear model predictive control algorithm has been designed and implemented to generate haptic feedback for lateral vehicle guidance, assisting the drivers in accomplishing their intended action. To validate the framework, operator-in-the-loop testing with 30 human subjects was conducted on a steer-by-wire platform with a virtual reality driving environment. The roadway scenarios included lane change, obstacle avoidance, intersection turns, and highway exit. The automated system with learning-based driver intent recognition was compared to both the automated system with a finite state machine-based driver intent estimator and the automated system without any driver intent prediction for all driving events. Test results demonstrate that semi-autonomous vehicle performance can be enhanced by up to 74.1% with a learning-based intent predictor. The proposed holistic framework that integrates human intelligence, machine learning algorithms, and vehicle control can help solve the driver-system conflict problem leading to safer vehicle operations

A Service Robot for Navigation Assistance and Physical Rehabilitation of Seniors

The population of the advanced countries is ageing, with the direct consequence that an increasing number of people will have to live with sensitive, cognitive and physical disabilities. People with impaired physical ability are not confident to move alone, especially in crowded environment and for long journeys, highly reducing the quality of their life. We propose a new generation of robotic walking assistants whose mechanical and electronic components are conceived to optimize the collaboration between the robot and its users. We will apply these general ideas to investigate the interaction between older adults and a robotic walker, named FriWalk, exploiting it either as a navigational or as a rehabilitation aid. For the use of the FriWalk as a navigation assistance, the system guides the user securing high levels of safety, a perfect compliance with the social rules and non-intrusive interaction between human and machine. To this purpose, we developed several guidance systems ranging from completely passive strategies to active solutions exploiting either the rear or the front motors mounted on the robot. The common strategy at the basis of all the algorithms is that the responsibility of the locomotion belongs always to the user, both to increase the mobility of elder users and to enhance their perception of control over the robot. This way the robot intervenes only whenever it is strictly necessary not to mitigate the user safety. Moreover, the robotic walker has been endowed with a tablet and graphical user interface (GUI) which provides the user with the visual indications about the path to follow. Since the FriWalk was developed to suit the needs of users with different deficits, we conducted extensive human-robot interaction (HRI) experiments with elders, complemented with direct interviews of the participants. As concerns the use of the FriWalk as a rehabilitation aid, force sensing to estimate the torques applied by the user and change the user perceived inertia can be exploited by doctors to let the user feel the device heavier or lighter. Moreover, thanks to a new generation of sensors, the device can be exploited in a clinical context to track the performance of the users' rehabilitation exercises, in order to assist nurses and doctors during the hospitalization of older adults

A Person-Centric Design Framework for At-Home Motor Learning in Serious Games

abstract: In motor learning, real-time multi-modal feedback is a critical element in guided training. Serious games have been introduced as a platform for at-home motor training due to their highly interactive and multi-modal nature. This dissertation explores the design of a multimodal environment for at-home training in which an autonomous system observes and guides the user in the place of a live trainer, providing real-time assessment, feedback and difficulty adaptation as the subject masters a motor skill. After an in-depth review of the latest solutions in this field, this dissertation proposes a person-centric approach to the design of this environment, in contrast to the standard techniques implemented in related work, to address many of the limitations of these approaches. The unique advantages and restrictions of this approach are presented in the form of a case study in which a system entitled the "Autonomous Training Assistant" consisting of both hardware and software for guided at-home motor learning is designed and adapted for a specific individual and trainer. In this work, the design of an autonomous motor learning environment is approached from three areas: motor assessment, multimodal feedback, and serious game design. For motor assessment, a 3-dimensional assessment framework is proposed which comprises of 2 spatial (posture, progression) and 1 temporal (pacing) domains of real-time motor assessment. For multimodal feedback, a rod-shaped device called the "Intelligent Stick" is combined with an audio-visual interface to provide feedback to the subject in three domains (audio, visual, haptic). Feedback domains are mapped to modalities and feedback is provided whenever the user's performance deviates from the ideal performance level by an adaptive threshold. Approaches for multi-modal integration and feedback fading are discussed. Finally, a novel approach for stealth adaptation in serious game design is presented. This approach allows serious games to incorporate motor tasks in a more natural way, facilitating self-assessment by the subject. An evaluation of three different stealth adaptation approaches are presented and evaluated using the flow-state ratio metric. The dissertation concludes with directions for future work in the integration of stealth adaptation techniques across the field of exergames.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Enhanced Virtuality: Increasing the Usability and Productivity of Virtual Environments

Mit stetig steigender Bildschirmauflösung, genauerem Tracking und fallenden Preisen stehen Virtual Reality (VR) Systeme kurz davor sich erfolgreich am Markt zu etablieren. Verschiedene Werkzeuge helfen Entwicklern bei der Erstellung komplexer Interaktionen mit mehreren Benutzern innerhalb adaptiver virtueller Umgebungen. Allerdings entstehen mit der Verbreitung der VR-Systeme auch zusätzliche Herausforderungen: Diverse Eingabegeräte mit ungewohnten Formen und Tastenlayouts verhindern eine intuitive Interaktion. Darüber hinaus zwingt der eingeschränkte Funktionsumfang bestehender Software die Nutzer dazu, auf herkömmliche PC- oder Touch-basierte Systeme zurückzugreifen. Außerdem birgt die Zusammenarbeit mit anderen Anwendern am gleichen Standort Herausforderungen hinsichtlich der Kalibrierung unterschiedlicher Trackingsysteme und der Kollisionsvermeidung. Beim entfernten Zusammenarbeiten wird die Interaktion durch Latenzzeiten und Verbindungsverluste zusätzlich beeinflusst. Schließlich haben die Benutzer unterschiedliche Anforderungen an die Visualisierung von Inhalten, z.B. Größe, Ausrichtung, Farbe oder Kontrast, innerhalb der virtuellen Welten. Eine strikte Nachbildung von realen Umgebungen in VR verschenkt Potential und wird es nicht ermöglichen, die individuellen Bedürfnisse der Benutzer zu berücksichtigen. Um diese Probleme anzugehen, werden in der vorliegenden Arbeit Lösungen in den Bereichen Eingabe, Zusammenarbeit und Erweiterung von virtuellen Welten und Benutzern vorgestellt, die darauf abzielen, die Benutzerfreundlichkeit und Produktivität von VR zu erhöhen. Zunächst werden PC-basierte Hardware und Software in die virtuelle Welt übertragen, um die Vertrautheit und den Funktionsumfang bestehender Anwendungen in VR zu erhalten. Virtuelle Stellvertreter von physischen Geräten, z.B. Tastatur und Tablet, und ein VR-Modus für Anwendungen ermöglichen es dem Benutzer reale Fähigkeiten in die virtuelle Welt zu übertragen. Des Weiteren wird ein Algorithmus vorgestellt, der die Kalibrierung mehrerer ko-lokaler VR-Geräte mit hoher Genauigkeit und geringen Hardwareanforderungen und geringem Aufwand ermöglicht. Da VR-Headsets die reale Umgebung der Benutzer ausblenden, wird die Relevanz einer Ganzkörper-Avatar-Visualisierung für die Kollisionsvermeidung und das entfernte Zusammenarbeiten nachgewiesen. Darüber hinaus werden personalisierte räumliche oder zeitliche Modifikationen vorgestellt, die es erlauben, die Benutzerfreundlichkeit, Arbeitsleistung und soziale Präsenz von Benutzern zu erhöhen. Diskrepanzen zwischen den virtuellen Welten, die durch persönliche Anpassungen entstehen, werden durch Methoden der Avatar-Umlenkung (engl. redirection) kompensiert. Abschließend werden einige der Methoden und Erkenntnisse in eine beispielhafte Anwendung integriert, um deren praktische Anwendbarkeit zu verdeutlichen. Die vorliegende Arbeit zeigt, dass virtuelle Umgebungen auf realen Fähigkeiten und Erfahrungen aufbauen können, um eine vertraute und einfache Interaktion und Zusammenarbeit von Benutzern zu gewährleisten. Darüber hinaus ermöglichen individuelle Erweiterungen des virtuellen Inhalts und der Avatare Einschränkungen der realen Welt zu überwinden und das Erlebnis von VR-Umgebungen zu steigern

Musical Haptics

Haptic Musical Instruments; Haptic Psychophysics; Interface Design and Evaluation; User Experience; Musical Performanc

Musical Haptics

Haptic Musical Instruments; Haptic Psychophysics; Interface Design and Evaluation; User Experience; Musical Performanc

Progress and Prospects of the Human-Robot Collaboration

International audienceRecent technological advances in hardware designof the robotic platforms enabled the implementationof various control modalities for improved interactions withhumans and unstructured environments. An important applicationarea for the integration of robots with such advancedinteraction capabilities is human-robot collaboration. Thisaspect represents high socio-economic impacts and maintainsthe sense of purpose of the involved people, as the robotsdo not completely replace the humans from the workprocess. The research community’s recent surge of interestin this area has been devoted to the implementation of variousmethodologies to achieve intuitive and seamless humanrobot-environment interactions by incorporating the collaborativepartners’ superior capabilities, e.g. human’s cognitiveand robot’s physical power generation capacity. In fact,the main purpose of this paper is to review the state-of-thearton intermediate human-robot interfaces (bi-directional),robot control modalities, system stability, benchmarking andrelevant use cases, and to extend views on the required futuredevelopments in the realm of human-robot collaboration