Using a 3DOF Parallel Robot and a Spherical Bat to hit a Ping-Pong Ball

Playing the game of Ping-Pong is a challenge to human abilities since it requires developing skills, such as fast reaction capabilities, precision of movement and high speed mental responses. These processes include the utilization of seven DOF of the human arm, and translational movements through the legs, torso, and other extremities of the body, which are used for developing different game strategies or simply imposing movements that affect the ball such as spinning movements. Computationally, Ping-Pong requires a huge quantity of joints and visual information to be processed and analysed, something which really represents a challenge for a robot. In addition, in order for a robot to develop the task mechanically, it requires a large and dexterous workspace, and good dynamic capacities. Although there are commercial robots that are able to play Ping-Pong, the game is still an open task, where there are problems to be solved and simplified. All robotic Ping-Pong players cited in the bibliography used at least four DOF to hit the ball. In this paper, a spherical bat mounted on a 3-DOF parallel robot is proposed. The spherical bat is used to drive the trajectory of a Ping-Pong ball.Fil: Trasloheros, Alberto. Universidad Aeronáutica de Querétaro; MéxicoFil: Sebastián, José María. Universidad Politécnica de Madrid; España. Consejo Superior de Investigaciones Científicas; EspañaFil: Torrijos, Jesús. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; EspañaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Roberti, Flavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

A 3-DoF Robotic Platform for the Rehabilitation and Assessment of Reaction Time and Balance Skills of MS Patients

The central nervous system (CNS) exploits anticipatory (APAs) and compensatory (CPAs) postural adjustments to maintain the balance.The postural adjustments comprising stability of the center of mass (CoM) and the pressure distribution of the body influence each other if there is a lack of performance in either of them.Any predictable or sudden perturbation may pave the way for the divergence of CoM from equilibrium and in homogeneous pressure distribution of the body.Such a situation is often observed in daily livings of Multiple Sclerosis (MS) patients owing to their poor APAs and CPAs, and induces their falls.The way of minimizing risk of falls in neurological patients is utilizing perturbation-based rehabilitation, as it is efficient in the recovery of the balance disorder.In the light of the findings, we present the design, implementation, and experimental evaluation of a novel 3 DoF parallel manipulator to treat the balance disorder of MS.The robotic platform allows angular motion of the ankle based on its anthropomorphic freedom.The end-effector endowed with upper and lower platforms is designed to evaluate both the pressure distribution of each foot and the CoM of the body, respectively.Data gathered from the platforms are utilized to both evaluate performance of the patients and used in high-level control of the robotic platform to regulate the difficulty level of tasks.In this study, kinematic and dynamic analyses of the robot are derived and validated in the simulation environment. Low-level control of the prototype is also successfully implemented through PID controller.The capacity of each platform is evaluated with a set of experiments considering assessment of pressure distribution and CoM of the foot-like-objects on the end-effector. Experimental results indicate that such a system well-address the need for balance skill training and assessment through the APAs and CPAs.Comment: 12 figures, 29 pages, PLOS ON

A neural network-based exploratory learning and motor planning system for co-robots

Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object

Electromyography Based Human-Robot Interfaces for the Control of Artificial Hands and Wearable Devices

The design of robotic systems is currently facing human-inspired solutions as a road to replicate the human ability and flexibility in performing motor tasks. Especially for control and teleoperation purposes, the human-in-the-loop approach is a key element within the framework know as Human-Robot Interface. This thesis reports the research activity carried out for the design of Human-Robot Interfaces based on the detection of human motion intentions from surface electromyography. The main goal was to investigate intuitive and natural control solutions for the teleoperation of both robotic hands during grasping tasks and wearable devices during elbow assistive applications. The design solutions are based on the human motor control principles and surface electromyography interpretation, which are reviewed with emphasis on the concept of synergies. The electromyography based control strategies for the robotic hand grasping and the wearable device assistance are also reviewed. The contribution of this research for the control of artificial hands rely on the integration of different levels of the motor control synergistic organization, and on the combination of proportional control and machine learning approaches under the guideline of user-centred intuitiveness in the Human-Robot Interface design specifications. From the side of the wearable devices, the control of a novel upper limb assistive device based on the Twisted String Actuation concept is faced. The contribution regards the assistance of the elbow during load lifting tasks, exploring a simplification in the use of the surface electromyography within the design of the Human-Robot Interface. The aim is to work around complex subject-dependent algorithm calibrations required by joint torque estimation methods

Sistema de aquisição de dados por interface háptica

Mestrado em Engenharia MecânicaNeste trabalho e apresentada uma interface háptica com realimentação de força para a teleoperação de um robô humanoide é que aborda um novo conceito destinado à aprendizagem por demonstração em robôs, denominado de ensino telecinestésico. A interface desenvolvida pretende promover o ensino cinestésico num ambiente de tele-robótica enriquecido pela virtualização háptica do ambiente e restrições do robô. Os dados recolhidos através desta poderão então ser usados em aprendizagem por demonstração, uma abordagem poderosa que permite aprender padrões de movimento sem a necessidade de modelos dinâmicos complexos, mas que geralmente é apresentada com demonstrações que não são fornecidas teleoperando os robôs. Várias experiências são referidas onde o ensino cinestésico em aprendizagem robótica foi utilizado com um sucesso considerável, bem como novas metodologias e aplicações com aparelhos hápticos. Este trabalho foi realizado com base na plataforma proprietária de 27 graus-de-liberdade do Projeto Humanoide da Universidade de Aveiro (PHUA), definindo novas methodologias de comando em tele-operação, uma nova abordagem de software e ainda algumas alterações ao hardware. Um simulador de corpo inteiro do robô em MATLAB SimMechanics é apresentado que é capaz de determinar os requisitos dinâmicos de binário de cada junta para uma dada postura ou movimento, exemplificando com um movimento efectuado para subir um degrau. Ir a mostrar algumas das potencialidades mas também algumas das limitações restritivas do software. Para testar esta nova abordagem tele-cinestésica são dados exemplos onde o utilizador pode desenvolver demonstrações interagindo fisicamente com o robô humanoide através de um joystick háptico PHANToM. Esta metodologia ir a mostrar que permite uma interação natural para o ensino e perceção tele-robóticos, onde o utilizador fornece instruções e correções funcionais estando ciente da dinâmica do sistema e das suas capacidades e limitações físicas. Ser a mostrado que a abordagem consegue atingir um bom desempenho mesmo com operadores inexperientes ou não familiarizados com o sistema. Durante a interação háptica, a informação sensorial e as ordens que guiam a uma tarefa específica podem ser gravados e posteriormente utilizados para efeitos de aprendizagem.In this work an haptic interface using force feedback for the teleoperation of a humanoid robot is presented, that approaches a new concept for robot learning by demonstration known as tele-kinesthethic teaching. This interface aims at promoting kinesthethic teaching in telerobotic environments enriched by the haptic virtualization of the robot's environment and restrictions. The data collected through this interface can later be in robot learning by demonstration, a powerful approach for learning motion patterns without complex dynamical models, but which is usually presented using demonstrations that are not provided by teleoperating the robots. Several experiments are referred where kinesthetic teaching for robot learning was used with considerable success, as well as other new methodologies and applications with haptic devices. This work was conducted on the proprietary 27 DOF University of Aveiro Humanoid Project (PHUA) robot, de ning new wiring and software solutions, as well as a new teleoperation command methodology. A MATLAB Sim- Mechanics full body robot simulator is presented that is able to determine dynamic joint torque requirements for a given robot movement or posture, exempli ed with a step climbing application. It will show some of the potentialities but also some restricting limitations of the software. To test this new tele-kinesthetic approach, examples are shown where the user can provide demonstrations by physically interacting with the humanoid robot through a PHANToM haptic joystick. This methodology will show that it enables a natural interface for telerobotic teaching and sensing, in which the user provides functional guidance and corrections while being aware of the dynamics of the system and its physical capabilities and / or constraints. It will also be shown that the approach can have a good performance even with inexperienced or unfamiliarized operators. During haptic interaction, the sensory information and the commands guiding the execution of a speci c task can be recorded and that data log from the human-robot interaction can be later used for learning purposes

Pathological Tremor as a Mechanical System: Modeling and Control of Artificial Muscle-Based Tremor Suppression

Central nervous system disorders produce the undesired, approximately rhythmic movement of body parts known as pathological tremor. This undesired motion inhibits the patient\u27s ability to perform tasks of daily living and participate in society. Typical treatments are medications and deep brain stimulation surgery, both of which include risks, side effects, and varying efficacy. Since the pathophysiology of tremor is not well understood, empirical investigation drives tremor treatment development. This dissertation explores tremor from a mechanical systems perspective to work towards theory-driven treatment design. The primary negative outcome of pathological tremor is the undesired movement of body parts: mechanically suppressing this motion provides effective tremor treatment by restoring limb function. Unlike typical treatments, the mechanisms for mechanical tremor suppression are well understood: applying joint torques that oppose tremor-producing muscular torques will reduce tremor irrespective of central nervous system pathophysiology. However, a tremor suppression system must also consider voluntary movements. For example, mechanically constraining the arm in a rigid cast eliminates tremor motion, but also eliminates the ability to produce voluntary motions. Indeed, passive mechanical systems typically reduce tremor and voluntary motions equally due to the close proximity of their frequency content. Thus, mechanical tremor suppression requires active actuation to reduce tremor with minimal influence on voluntary motion. However, typical engineering actuators are rigid and bulky, preventing clinical implementations. This dissertation explores dielectric elastomers as tremor suppression actuators to improve clinical implementation potential of mechanical tremor suppression. Dielectric elastomers are often called artificial muscles due to their similar mechanical properties as human muscle; these similarities may enable relatively soft, low-profile implementations. The primary drawback of dielectric elastomers is their relatively low actuation levels compared to typical actuators. This research develops a tremor-active approach to dielectric elastomer-based tremor suppression. In a tremor-active approach, the actuators only actuate to oppose tremor, while the human motor system must overcome the passive actuator dynamics. This approach leverages the low mechanical impedance of dielectric elastomers to overcome their low actuation levels. Simulations with recorded tremor datasets demonstrate excellent and robust tremor suppression performance. Benchtop experiments validate the control approach on a scaled system. Since dielectric elastomers are not yet commercially available, this research quantifies the necessary dielectric elastomer parameters to enable clinical implementations and evaluates the potential of manufacturing approaches in the literature to achieve these parameters. Overall, tremor-active control using dielectric elastomers represents a promising alternative to medications and surgery. Such a system may achieve comparable tremor reduction as medications and deep brain stimulation with minimal risks and greater efficacy, but at the cost of increased patient effort to produce voluntary motions. Parallel advances in scaled dielectric elastomer manufacturing processes and high-voltage power electronics will enable consumer implementations. In addition to tremor suppression, this dissertation investigates the mechanisms of central nervous system tremor generation from a control systems perspective. This research investigates a delay-based model for parkinsonian tremor. Besides tremor, Parkinson\u27s disease generally inhibits movement, with typical symptoms including rigidity, bradykinesia, and increased reaction times. This fact raises the question as to how the same disease produces excessive movement (tremor) despite characteristically inhibiting movement. One possible answer is that excessive central nervous system inhibition produces unaccounted feedback delays that cause instability. This dissertation develops an optimal control model of human motor control with an unaccounted delay between the state estimator and controller. This delay represents the increased inhibition projected from the basal ganglia to the thalamus, delaying signals traveling from the cerebellum (estimator) to the primary motor cortex (controller). Model simulations show increased delays decrease tremor frequency and increase tremor amplitude, consistent with the evolution of tremor as the disease progresses. Simulations that incorporate tremor resetting and random variation in control saturation produce simulated tremor with similar characteristics as recorded tremor. Delay-induced tremor explains the effectiveness of deep brain stimulation in both the thalamus and basal ganglia since both regions contribute to the presence of feedback delay. Clinical evaluation of mechanical tremor suppression may provide clinical evidence for delay-induced tremor: unlike state-independent tremor, suppression of delay-induced tremor increases tremor frequency. Altogether, establishing the mechanisms for tremor generation will facilitate pathways towards improved treatments and cure development

Design and development of robust hands for humanoid robots

Design and development of robust hands for humanoid robot

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

A brain-machine interface for assistive robotic control

Brain-machine interfaces (BMIs) are the only currently viable means of communication for many individuals suffering from locked-in syndrome (LIS) – profound paralysis that results in severely limited or total loss of voluntary motor control. By inferring user intent from task-modulated neurological signals and then translating those intentions into actions, BMIs can enable LIS patients increased autonomy. Significant effort has been devoted to developing BMIs over the last three decades, but only recently have the combined advances in hardware, software, and methodology provided a setting to realize the translation of this research from the lab into practical, real-world applications. Non-invasive methods, such as those based on the electroencephalogram (EEG), offer the only feasible solution for practical use at the moment, but suffer from limited communication rates and susceptibility to environmental noise. Maximization of the efficacy of each decoded intention, therefore, is critical. This thesis addresses the challenge of implementing a BMI intended for practical use with a focus on an autonomous assistive robot application. First an adaptive EEG- based BMI strategy is developed that relies upon code-modulated visual evoked potentials (c-VEPs) to infer user intent. As voluntary gaze control is typically not available to LIS patients, c-VEP decoding methods under both gaze-dependent and gaze- independent scenarios are explored. Adaptive decoding strategies in both offline and online task conditions are evaluated, and a novel approach to assess ongoing online BMI performance is introduced. Next, an adaptive neural network-based system for assistive robot control is presented that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. Exploratory learning, or “learning by doing,” is an unsupervised method in which the robot is able to build an internal model for motor planning and coordination based on real-time sensory inputs received during exploration. Finally, a software platform intended for practical BMI application use is developed and evaluated. Using online c-VEP methods, users control a simple 2D cursor control game, a basic augmentative and alternative communication tool, and an assistive robot, both manually and via high-level goal-oriented commands