Este trabalho apresenta uma comparação entre as estratégias de controle por torque calculado e controle repetitivo aplicadas a manipuladores robóticos. O objetivo no uso desses controladores é para que o manipulador siga referência de trajetória periódica no espaço das juntas. O desenvolvimento e implementação dos controladores são focados no manipulador WAM (Whole Arm Manipulator) da Barrett Technology®Inc. Neste trabalho, também são apresentadas uma formulação do modelo não linear do manipulador e as sínteses dos controladores por torque calculado e repetitivo aplicados ao modelo do manipulador linearizado por realimentação. O controlador por torque calculado é apresentado e sintetizado na sua forma clássica. Para o controlador repetitivo, a síntese parte do princípio do modelo interno com a adição de uma estrutura repetitiva e uma realimentação proporcional e derivativa do erro de seguimento de referência O projeto dos ganhos do controlador repetitivo é feito através de um problema de otimização convexa com restrições na forma de inequações matriciais lineares (ou no inglês: Linear Matrix Inequalities - LMI). A formulação do problema de otimização parte da teoria de estabilidade segundo Lyapunov com um funcional Lyapunov-Krasoviskii, adição de um custo quadrático, para ajuste de desempenho, e de um critério de desempenho transitório dado pela taxa de decaimento exponencial da norma dos estados. É apresentada a comparação entre as estratégias de controle e a validação do controlador repetitivo proposto aplicado ao caso com linearização perfeita e ao caso com o modelo não linear do manipulador. No primeiro caso, é feita a simulação do modelo linear do manipulador com adição de um torque de atrito na junta. No segundo caso, é utilizado o sistema ROS (Robot Operating System) com o programa Gazebo simulando o manipulador WAM considerando erros de linearização, isto é, incertezas paramétricas. / This work presents a comparison between the strategies of computed-torque control and repetitive control applied to robotic manipulators. The main objective in use these controllers with the manipulator is to tracking periodic trajectory in joint space. The development and implementation of controllers are focused on the Whole Arm Manipulator (WAM) of the Barrett Technology®Inc. Also featured are a non-linear model formulation of the manipulator and the synthesis of controllers for computed-torque control and repetitive control applied to the manipulator model linearized by state feedback. The computed-torque controller is presented in its classic form. For the repetitive controller, the synthesis is based on the internal model principle with the addition of a repetitive structure and a proportional-derivative reference tracking error feedback. The design of the repetitive controller gains is done through a convex optimization problem with linear matrix inequalities (LMI) constraints. The formulation of the optimization problem is based on the Lyapunov stability theory using a Lyapunov-Krasoviskii functional, addition of a quadratic cost for performance adjustment and a transient performance criteria given by the exponential decay rate of the states norm. A comparison between the control strategies and the validation of the repetitive controller applied to the case with perfect linearization and the case with the non-linear model of the manipulator are presented. In the first case, is made simulations of the linear model of the manipulator in MATLAB program, with the addition of a disturbance modeling the friction torque at the joint. In the second case, is used the Robot Operating System (ROS) with Gazebo program simulating the WAM nonlinear model. In this case, a possible mismatch between the model used for the feedback linearization and the real system is taken into account.

Over the last decades, the worldwide increase in cases of neuromotor health issues, due to overall population aging, motivated a growing research interest in improving rehabilitation processes with robotics. The performed studies opened the possibility to include other auxiliary technologies in physical and occupational therapy, like virtual reality and remote interaction between patients and therapists. Although there are verified and potential benefits to rehabilitation provided by these techniques, there are still few attempts towards tooling and methodology standardization, which could lead to faster developments. This work describes the creation of a proposed common computational platform for robotic rehabilitation studies, with support for virtual games and telecommunication. A preceding literature review helped to determine the requirements and development tools for such multipurpose system. Its modular and configurable design aims to allow components reuse and adaptability to different robotic therapy cases, even ones not initially intended, preventing work duplication. Details about the system\'s structure, components and operation are shown, focusing its provided flexibility. Multiplayer games involving position and force control are also created to test the proposed system in real and simulated environment, in order to demonstrate its usability for application and evaluation of robotic rehabilitation strategies. At the end, the results obtained so far are discussed and considerations about missing points and future developments are made. / Nas últimas décadas, o aumento global nos casos de problemas de saúde neuromotores, devido ao envelhecimento da população, motivou um interesse crescente na pesquisa sobre melhoria de processos de rehabilitação utilizando robótica. Os estudos realizados realizados abriram possibilidade de se incluir outras tecnologias auxiliares na terapia física e ocupacional, como realidade virtual e interação remota entre pacientes e terapeutas. Apesar dos benefícios verificados ou potenciais da aplicação dessas técnicas, ainda há poucas iniciativas no sentido de padronizar ferramentas e metodologias para sua implementação e teste, o que poderia catalisar os avanços na área. Esse trabalho descreve a criação de uma plataforma computacional comum para estudos de Reabilitação Robótica, com suporte a utilização de Jogos Sérios e teleoperação. Uma revisão bibliográfica prévia ajudou a definir os requisitos e ferramentas de desenvolvimento adequadas para tal sistema multipropósito. Seu projeto modular e configurável tem o intuito de permitir reutilização de componentes e sua fácil adaptação a diferentes tipos de terapia, mesmo não inicialmente planejadas, evitando duplicação de trabalho. Jogos multijogador envolvendo controle de força e posição são também criados para testar o sistema proposto em situações reais ou simuladas, de modo a demonstrar sua utilidade para aplicação Detalhes de sua estrutura de operação, protocolos de comunicação e componentes são mostrados, destacando-se a flexibilidade oferecida. Testes simples de viabilidade com indivíduos saudáveis são realizados, a fim de demonstrar sua utilidade para aplicação e avaliação de estratégias de reabilitação robótica. Ao fim, os resultados obtidos até então são discutidos, e considerações sobre informações ainda faltantes e trabalhos futuros são feitas.

<div>There are millions of individuals in the world who currently experience limited mobility as a result of aging, stroke, injuries to the brain or spinal cord, and certain neurological diseases. Robotic Assistive Devices (RADs) have shown superiority in helping people with limited mobility by providing physical movement assistance. However, RADs currently existing on the market for people with limited mobility are still far from intelligent.</div><div><br></div><div>Learning control strategies are developed in this study to make a Cable-Driven Assistive Device (CDAD) intelligent in assisting a human joint (e.g., a knee joint, an ankle joint, or a wrist joint). CDADs are a type of RADs designed based on Cable-Driven Parallel Robots (CDPRs). A PID–FNN control strategy and DDPG-based strategies are proposed to allow a CDAD to learn physical human-robot interactions when controlling the pose of the human joint. Both pose-tracking and trajectory-tracking tasks are designed to evaluate the PID–FNN control strategy and the DDPG-based strategies through simulations. Simulations are conducted in the Gazebo simulator using an example CDAD with three degrees of freedom and four cables. Simulation results show that the proposed PID–FNN control strategy and DDPG-based strategies work in controlling a CDAD with proper learning.</div>

Dans cette thèse, nous proposons d'intégrer la notion d'habitude comportementale au sein d'une architecture de contrôle robotique, et d'étudier son interaction avec les mécanismes générant le comportement planifié. Les architectures de contrôle robotiques permettent à ce dernier d'être utilisé efficacement dans le monde réel et au robot de rester réactif aux changements dans son environnement, tout en étant capable de prendre des décisions pour accomplir des buts à long terme (Kortenkamp et Simmons, 2008). Or, ces architectures sont rarement dotées de capacités d'apprentissage leur permettant d'intégrer les expériences précédentes du robot. En neurosciences et en psychologie, l'étude des différents types d'apprentissage montre pour que ces derniers sont une capacité essentielle pour adapter le comportement des mammifères à des contextes changeants, mais également pour exploiter au mieux les contextes stables (Dickinson, 1985). Ces apprentissages sont modélisés par des algorithmes d'apprentissage par renforcement direct et indirect (Sutton et Barto, 1998), combinés pour exploiter leurs propriétés au mieux en fonction du contexte (Daw et al., 2005). Nous montrons que l'architecture proposée, qui s'inspire de ces modèles du comportement, améliore la robustesse de la performance lors d'un changement de contexte dans une tâche simulée. Si aucune des méthodes de combinaison évaluées ne se démarque des autres, elles permettent d'identifier les contraintes sur le processus de planification. Enfin, l'extension de l'étude de notre architecture à deux tâches (dont l'une sur robot réel) confirme que la combinaison permet l'amélioration de l'apprentissage du robot. / In this work, we study how the notion of behavioral habit, inspired from the study of biology, can benefit to robots. Robot control architectures allow the robot to be able to plan to reach long term goals while staying reactive to events happening in the environment (Kortenkamp et Simmons, 2008). However, these architectures are rarely provided with learning capabilities that would allow them to acquire knowledge from experience. On the other hand, learning has been shown as an essential abiilty for behavioral adaptation in mammals. It permits flexible adaptation to new contexts but also efficient behavior in known contexts (Dickinson, 1985). The learning mechanisms are modeled as model-based (planning) and model-free (habitual) reinforcement learning algorithms (Sutton et Barto, 1998) which are combined into a global model of behavior (Daw et al., 2005). We proposed a robotic control architecture that take inspiration from this model of behavior and embed the two kinds of algorithms, and studied its performance in a robotic simulated task. None of the several methods for combining the algorithm we studied gave satisfying results, however, it allowed to identify some properties required for the planning process in a robotic task. We extended our study to two other tasks (one being on a real robot) and confirmed that combining the algorithms improves learning of the robot's behavior.

Human-controlled robots are utilized in many situations and such use is becoming widespread. This thesis details research that allows a single human to interact with a team of robots performing tasks that require cooperation. The research provides insight into effective interaction design methods and appropriate interface techniques. The use of team-level autonomy is shown to decrease human workload while simultaneously improving individual robot efficiency and robot-team cooperation. An indoor human-robot interaction testbed was developed at the BYU MAGICC Lab to facilitate experimentation. The testbed consists of eight robots equipped with wireless modems, a field on which the robots move, an overhead camera and image processing software which tracks robot position and heading, a simulator which allows development and testing without hardware utilization and a graphical user interface which enables human control of either simulated or hardware robots. The image processing system was essential for effective robot hardware operation and is described in detail. The system produced accurate robot position and heading information 30 times per second for a maximum of 12 robots, was relatively insensitive to lighting conditions and was easily reconfigurable. The completed testbed was utilized to create a game for testing human-robot interaction schemes. The game required a human controlling three robots to find and tag three robot opponents in a maze. Finding an opponent could be accomplished by individual robots, but tagging an opponent required cooperation between at least two robots. The game was played by 11 subjects in five different autonomy modes ranging from limited robot autonomy to advanced individual autonomy with basic team-level autonomy. Participants were interrupted during the game by a secondary spatial reasoning task which prevented them from interacting with the robots for short periods of time. Robot performance during that interruption provided a measure of both individual and team neglect tolerance. Individual robot neglect tolerance and performance did not directly correspond to those quantities at the team level. The interaction mode with the highest levels of individual and team autonomy was most effective; it minimized game time and human workload and maximized team neglect tolerance.

As an attempt to steer away from developing an autonomous robot with complex centralised intelligence, this thesis proposes an intelligent environment infrastructure where intelligences are distributed in the environment through collaborative vision sensors mounted in a physical architecture, forming a wireless sensor network, to enable the navigation of unintelligent robots within that physical architecture. The aim is to avoid the bottleneck of centralised robot intelligence that hinders the application and exploitation of autonomous robot. A bio-mimetic snake algorithm is proposed to coordinate the distributed vision sensors for the generation of a collision free Reference-snake (R-snake) path during the path planning process. By following the R-snake path, a novel Accompanied snake (A-snake) method that complies with the robot's nonholonomic constraints for trajectory generation and motion control is introduced to generate real time robot motion commands to navigate the robot from its current position to the target position. A rolling window optimisation mechanism subject to control input saturation constraints is carried out for time-optimal control along the A-snake. A comprehensive simulation software and a practical distributed intelligent environment with vision sensors mounted on a building ceiling are developed. All the algorithms proposed in this thesis are first verified by the simulation and then implemented in the practical intelligent environment. A model car with less on-board intelligence is successfully controlled by the distributed vision sensors and demonstrated superior mobility.

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In der vorliegenden Arbeit wird die flexible Steuerungsarchitektur Apeca für Systeme der Robotik sowie der robotergestützten Fertigungstechnik vorgestellt. Dafür werden verschiedene Anforderungen identifiziert und innerhalb eines Entwurfs vereint. Ein Hauptaugenmerk des dabei entstandenen Konzeptes ist es, einen vereinfachten Engineeringprozess für den Steuerungsentwurf zu ermöglichen. Dieser Ansatz wird durch die Verwendung von Aktionsprimitiven ermöglicht, die in Form atomarer Systemverhalten in einer speziellen Modulhierarchie eingesetzt werden. Hierzu erfolgt innerhalb der Steuerungsarchitektur eine Trennung zwischen einem funktionsorientierten verhaltensbasierten Modell zur hierarchischen sowie funktionell parallelen Ausführung von Aktionsprimitiven und einem ablauforientierten Modell zur aufgabenabhängigen Aktivierung derselben. Mit Hilfe eines Nutzerkonzepts werden diese Modelle verschiedenen Anwendern zugeordnet. Die objektorientierte Realisierung dieses Entwurfs ermöglicht die Verwendung und Synchronisation von mehreren Teilsystemen innerhalb einer Steuerung. In der Arbeit wird sowohl der entstandene Entwurf diskutiert als auch eine prototypische Implementierung vorgestellt. Abschließend werden die Ergebnisse anhand verschiedener Demonstrationsszenarien präsentiert. / In this present work, the Apeca framework, a flexible control architecture for robotic systems, is introduced. The conceptual design combines different requirements identified in miscellaneous robotic control approaches. The main focus of the resulting concept is on a simplified engineering process for the controller design. This approach is supported by the use of atomic system behaviors, the so called action primitives, in a special module hierarchy. For this purpose a distinction between a functional behavior based system model with hierarchically and also parallelly executed action primitives and a sequential control system model with a task-dependent activation of the primitives is proposed. These models are assigned to different users through a distinct user concept. An object-oriented implementation of the proposed architecture allows the utilization and synchronisation of multiple (sub-)systems within one framework. In this work the proposed framework will be discussed, a prototypical implementation will be presented and results based on different experimental scenarios will be shown.

Der vorliegende Beitrag stellt einen Entwurf für eine flexible und robuste Steuerungsarchitektur für Roboter- und Fertigungssysteme vor. Dabei wurde versucht ein offenes Konzept zu realisieren, welches einen vereinfachten Engineeringprozess ermöglicht. Hierzu wird innerhalb der Steuerung eine Trennung zwischen einem funktionellen verhaltensbasierten und einem ablauforientierten Modell vorgeschlagen. Dieser Ansatz wird durch die Verwendung von Aktionsprimitiven innerhalb einer hybriden Robotersteuerung ermöglicht. Diese garantieren durch ihre ausgeprägte Modularität eine hohe Flexibilität und Erweiterbarkeit des entstandenen Systems. Im Beitrag wird sowohl der entstandene Entwurf diskutiert als auch eine prototypische objektorientierte Implementierung vorgestellt sowie erste Ergebnisse präsentiert. / This paper presents a framework for a flexible and robust control architecture for robotic systems. The design incorporates an application independent system concept which allows a simplified engineering process. For this purpose a distinction between a functional behavioural and a sequential control system model is proposed. This approach is based on the utilisation of action primitives within a hybrid control architecture. The use of these primitives affords a high level of modularity through increasing flexibility and expandability of the resulting system. In this paper the proposed framework will be discussed as well as a prototypical object-oriented implementation and first results.