89 research outputs found

    Hopping peak height algorithm for a one legged robot hopping height control

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    This paper presents the hopping peak height algorithm in controlling the hopping height of a one legged hopping robot. The hopping mechanism produces continuous and rhythmic pattern. The continuous and rhythmic pattern behaviors produce oscillation feedback to the closed loop system and continuously produce oscillation error to the controller. Therefore, hopping peak height algorithm is designed and embedded into the closed loop control system feedback to determine the hopping peak of each produced hopping as a feedback. The existence of the hopping peak height algorithm assists the PI-CPG controller to converge the hopping height error approximately to zero

    Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics

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    Properly designing a system to exhibit favorable natural dynamics can greatly simplify designing or learning the control policy. However, it is still unclear what constitutes favorable natural dynamics and how to quantify its effect. Most studies of simple walking and running models have focused on the basins of attraction of passive limit-cycles and the notion of self-stability. We instead emphasize the importance of stepping beyond basins of attraction. We show an approach based on viability theory to quantify robust sets in state-action space. These sets are valid for the family of all robust control policies, which allows us to quantify the robustness inherent to the natural dynamics before designing the control policy or specifying a control objective. We illustrate our formulation using spring-mass models, simple low dimensional models of running systems. We then show an example application by optimizing robustness of a simulated planar monoped, using a gradient-free optimization scheme. Both case studies result in a nonlinear effective stiffness providing more robustness.Comment: 15 pages. This work has been accepted to IEEE Transactions on Robotics (2019

    Bio-inspired neuromuscular reflex based hopping controller for a segmented robotic leg

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    It has been shown that human-like hopping can be achieved by muscle reflex control in neuromechanical simulations. However, it is unclear if this concept is applicable and feasible for controlling a real robot. This paper presents a low-cost two-segmented robotic leg design and demonstrates the feasibility and the benefits of the bio-inspired neuromuscular reflex based control for hopping. Simulation models were developed to describe the dynamics of the real robot. Different neuromuscular reflex pathways were investigated with the simulation models. We found that stable hopping can be achieved with both positive muscle force and length feedback, and the hopping height can be controlled by modulating the muscle force feedback gains with the return maps. The force feedback neuromuscular reflex based controller is robust against body mass and ground impedance changes. Finally, we implemented the controller on the real robot to prove the feasibility of the proposed neuromuscular reflex based control idea. This paper demonstrates the neuromuscular reflex based control approach is feasible to implement and capable of achieving stable and robust hopping in a real robot. It provides a promising direction of controlling the legged robot to achieve robust dynamic motion in the future

    Bioinspired template-based control of legged locomotion

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    cient and robust locomotion is a crucial condition for the more extensive use of legged robots in real world applications. In that respect, robots can learn from animals, if the principles underlying locomotion in biological legged systems can be transferred to their artificial counterparts. However, legged locomotion in biological systems is a complex and not fully understood problem. A great progress to simplify understanding locomotion dynamics and control was made by introducing simple models, coined ``templates'', able to represent the overall dynamics of animal (including human) gaits. One of the most recognized models is the spring-loaded inverted pendulum (SLIP) which consists of a point mass atop a massless spring. This model provides a good description of human gaits, such as walking, hopping and running. Despite its high level of abstraction, it supported and inspired the development of successful legged robots and was used as explicit targets for control, over the years. Inspired from template models explaining biological locomotory systems and Raibert's pioneering legged robots, locomotion can be realized by basic subfunctions: (i) stance leg function, (ii) leg swinging and (iii) balancing. Combinations of these three subfunctions can generate different gaits with diverse properties. Using the template models, we investigate how locomotor subfunctions contribute to stabilize different gaits (hopping, running and walking) in different conditions (e.g., speeds). We show that such basic analysis on human locomotion using conceptual models can result in developing new methods in design and control of legged systems like humanoid robots and assistive devices (exoskeletons, orthoses and prostheses). This thesis comprises research in different disciplines: biomechanics, robotics and control. These disciplines are required to do human experiments and data analysis, modeling of locomotory systems, and implementation on robots and an exoskeleton. We benefited from facilities and experiments performed in the Lauflabor locomotion laboratory. Modeling includes two categories: conceptual (template-based, e.g. SLIP) models and detailed models (with segmented legs, masses/inertias). Using the BioBiped series of robots (and the detailed BioBiped MBS models; MBS stands for Multi-Body-System), we have implemented newly-developed design and control methods related to the concept of locomotor subfunctions on either MBS models or on the robot directly. In addition, with involvement in BALANCE project (\url{http://balance-fp7.eu/}), we implemented balance-related control approaches on an exoskeleton to demonstrate their performance in human walking. The outcomes of this research includes developing new conceptual models of legged locomotion, analysis of human locomotion based on the newly developed models following the locomotor subfunction trilogy, developing methods to benefit from the models in design and control of robots and exoskeletons. The main contribution of this work is providing a novel approach for modular control of legged locomotion. With this approach we can identify the relation between different locomotor subfunctions e.g., between balance and stance (using stance force for tuning balance control) or balance and swing (two joint hip muscles can support the swing leg control relating it to the upper body posture) and implement the concept of modular control based on locomotor subfunctions with a limited exchange of sensory information on several hardware platforms (legged robots, exoskeleton)

    Viability in State-Action Space: Connecting Morphology, Control, and Learning

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    Wie können wir Robotern ermöglichen, modellfrei und direkt auf der Hardware zu lernen? Das maschinelle Lernen nimmt als Standardwerkzeug im Arsenal des Robotikers seinen Platz ein. Es gibt jedoch einige offene Fragen, wie man die Kontrolle über physikalische Systeme lernen kann. Diese Arbeit gibt zwei Antworten auf diese motivierende Frage. Das erste ist ein formales Mittel, um die inhärente Robustheit eines gegebenen Systemdesigns zu quantifizieren, bevor der Controller oder das Lernverfahren entworfen wird. Dies unterstreicht die Notwendigkeit, sowohl das Hardals auch das Software-Design eines Roboters zu berücksichtigen, da beide Aspekte in der Systemdynamik untrennbar miteinander verbunden sind. Die zweite ist die Formalisierung einer Sicherheitsmass, die modellfrei erlernt werden kann. Intuitiv zeigt diese Mass an, wie leicht ein Roboter Fehlschläge vermeiden kann. Auf diese Weise können Roboter unbekannte Umgebungen erkunden und gleichzeitig Ausfälle vermeiden. Die wichtigsten Beiträge dieser Dissertation basieren sich auf der Viabilitätstheorie. Viabilität bietet eine alternative Sichtweise auf dynamische Systeme: Anstatt sich auf die Konvergenzeigenschaften eines Systems in Richtung Gleichgewichte zu konzentrieren, wird der Fokus auf Menge von Fehlerzuständen und die Fähigkeit des Systems, diese zu vermeiden, verlagert. Diese Sichtweise eignet sich besonders gut für das Studium der Lernkontrolle an Robotern, da Stabilität im Sinne einer Konvergenz während des Lernprozesses selten gewährleistet werden kann. Der Begriff der Viabilität wird formal auf den Zustand-Aktion-Raum erweitert, mit Viabilitätsmengen von Staat-Aktionspaaren. Eine über diese Mengen definierte Mass ermöglicht eine quantifizierte Bewertung der Robustheit, die für die Familie aller fehlervermeidenden Regler gilt, und ebnet den Weg für ein sicheres, modellfreies Lernen. Die Arbeit beinhaltet auch zwei kleinere Beiträge. Der erste kleine Beitrag ist eine empirische Demonstration der Shaping durch ausschliessliche Modifikation der Systemdynamik. Diese Demonstration verdeutlicht die Bedeutung der Robustheit gegenüber Fehlern für die Lernkontrolle: Ausfälle können nicht nur Schäden verursachen, sondern liefern in der Regel auch keine nützlichen Gradienteninformationen für den Lernprozess. Der zweite kleine Beitrag ist eine Studie über die Wahl der Zustandsinitialisierungen. Entgegen der Intuition und der üblichen Praxis zeigt diese Studie, dass es zuverlässiger sein kann, das System gelegentlich aus einem Zustand zu initialisieren, der bekanntermassen unkontrollierbar ist.How can we enable robots to learn control model-free and directly on hardware? Machine learning is taking its place as a standard tool in the roboticist’s arsenal. However, there are several open questions on how to learn control for physical systems. This thesis provides two answers to this motivating question. The first is a formal means to quantify the inherent robustness of a given system design, prior to designing the controller or learning agent. This emphasizes the need to consider both the hardware and software design of a robot, which are inseparably intertwined in the system dynamics. The second is the formalization of a safety-measure, which can be learned model-free. Intuitively, this measure indicates how easily a robot can avoid failure, and enables robots to explore unknown environments while avoiding failures. The main contributions of this dissertation are based on viability theory. Viability theory provides a slightly unconventional view of dynamical systems: instead of focusing on a system’s convergence properties towards equilibria, the focus is shifted towards sets of failure states and the system’s ability to avoid these sets. This view is particularly well suited to studying learning control in robots, since stability in the sense of convergence can rarely be guaranteed during the learning process. The notion of viability is formally extended to state-action space, with viable sets of state-action pairs. A measure defined over these sets allows a quantified evaluation of robustness valid for the family of all failure-avoiding control policies, and also paves the way for enabling safe model-free learning. The thesis also includes two minor contributions. The first minor contribution is an empirical demonstration of shaping by exclusively modifying the system dynamics. This demonstration highlights the importance of robustness to failures for learning control: not only can failures cause damage, but they typically do not provide useful gradient information for the learning process. The second minor contribution is a study on the choice of state initializations. Counter to intuition and common practice, this study shows it can be more reliable to occasionally initialize the system from a state that is known to be uncontrollable

    Modular Hopping and Running via Parallel Composition

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    Though multi-functional robot hardware has been created, the complexity in its functionality has been constrained by a lack of algorithms that appropriately manage flexible and autonomous reconfiguration of interconnections to physical and behavioral components. Raibert pioneered a paradigm for the synthesis of planar hopping using a composition of ``parts\u27\u27: controlled vertical hopping, controlled forward speed, and controlled body attitude. Such reduced degree-of-freedom compositions also seem to appear in running animals across several orders of magnitude of scale. Dynamical systems theory can offer a formal representation of such reductions in terms of ``anchored templates,\u27\u27 respecting which Raibert\u27s empirical synthesis (and the animals\u27 empirical performance) can be posed as a parallel composition. However, the orthodox notion (attracting invariant submanifold with restriction dynamics conjugate to a template system) has only been formally synthesized in a few isolated instances in engineering (juggling, brachiating, hexapedal running robots, etc.) and formally observed in biology only in similarly limited contexts. In order to bring Raibert\u27s 1980\u27s work into the 21st century and out of the laboratory, we design a new family of one-, two-, and four-legged robots with high power density, transparency, and control bandwidth. On these platforms, we demonstrate a growing collection of {\{body, behavior}\} pairs that successfully embody dynamical running / hopping ``gaits\u27\u27 specified using compositions of a few templates, with few parameters and a great deal of empirical robustness. We aim for and report substantial advances toward a formal notion of parallel composition---embodied behaviors that are correct by design even in the presence of nefarious coupling and perturbation---using a new analytical tool (hybrid dynamical averaging). With ideas of verifiable behavioral modularity and a firm understanding of the hardware tools required to implement them, we are closer to identifying the components required to flexibly program the exchange of work between machines and their environment. Knowing how to combine and sequence stable basins to solve arbitrarily complex tasks will result in improved foundations for robotics as it goes from ad-hoc practice to science (with predictive theories) in the next few decades

    Climbing and Walking Robots

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    Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study

    Control of underactuated planar pronking through an embedded spring-mass Hopper template

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    Autonomous use of legged robots in unstructured, outdoor settings requires dynamically dexterous behaviors to achieve sufficient speed and agility without overly complex and fragile mechanics and actuation. Among such behaviors is the relatively under-studied pronking (aka. stotting), a dynamic gait in which all legs are used in synchrony, usually resulting in relatively slow speeds but long flight phases and large jumping heights. Instantiations of this gait for robotic systems have been mostly limited to open-loop strategies, suffering from severe pitch instability for underactuated designs due to the lack of active feedback. However, both the kinematic simplicity of this gait and its dynamic nature suggest that the Spring-Loaded Inverted Pendulum model (SLIP) would be a good basis for the implementation of a more robust feedback controller for pronking. In this paper, we describe how template-based control, a controller structure based on the embedding of a simple dynamical "template" within a more complex "anchor" system, can be used to achieve very stable pronking for a planar, underactuated hexapod robot. In this context, high-level control of the gait is regulated through speed and height commands to the SLIP template, while the embedding controller ensures the stability of the remaining degrees of freedom. We use simulation studies to show that unlike existing open-loop alternatives, the resulting control structure provides explicit gait control authority and significant robustness against sensor and actuator noise. © 2010 Springer Science+Business Media, LLC

    Hopping, Landing, and Balancing with Springs

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    This work investigates the interaction of a planar double pendulum robot and springs, where the lower body (the leg) has been modified to include a spring-loaded passive prismatic joint. The thesis explores the mechanical advantage of adding a spring to the robot in hopping, landing, and balancing activities by formulating the motion problem as a boundary value problem; and also provides a control strategy for such scenarios. It also analyses the robustness of the developed controller to uncertain spring parameters, and an observer solution is provided to estimate these parameters while the robot is performing a tracking task. Finally, it shows a study of how well IMUs perform in bouncing conditions, which is critical for the proper operation of a hopping robot or a running-legged one

    Towards Testable Neuromechanical Control of Architectures for Running

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    Our objective is to provide experimentalists with neuromechanical control hypotheses that can be tested with kinematic data sets. To illustrate the approach, we select legged animals responding to perturbations during running. In the following sections, we briefly outline our dynamical systems approach, state our over-arching hypotheses, define four neuromechanical control architectures (NCAs) and conclude by proposing a series of perturbation experiments that can begin to identify the simplest architecture that best represents an animal\u27s controller
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