Simulation-based functional evaluation of anthropomorphic artificial hands.

This thesis proposes an outline for a framework for an evaluation method that takes as an input a model of an artificial hand, which claims to be anthropomorphic, and produces as output the set of tasks that the hand can perform. The framework is based on studying the literature on the anatomy and functionalities of the human hand and methods of implementing these functionalities in artificial systems. The thesis also presents a partial implementation of the framework which focuses on tasks of gesturing and grasping using anthropomorphic postures. This thesis focuses on the evaluation of the intrinsic hardware of robot hands from technical and functional perspectives, including kinematics of the mechanical structure, geometry of the contact surface, and functional force conditions for successful grasps. This thesis does not consider topics related to control or elements of aesthetics of the design of robot hands.The thesis reviews the literature on the anatomy, motion and sensory capabilities, and functionalities of the human hand to define a reference to evaluate artificial hands. It distinguishes between the hand's construction and functionalities and presents a discussion of anthropomorphism that reflects this distinction. It reviews key theory related to artificial hands and notable solutions and existing methods of evaluating artificial hands.The thesis outlines the evaluation framework by defining the action manifold of the anthropomorphic hand, defined as the set of all tasks that a hypothetical ideal anthropomorphic hand should be able to do, and analysing the manifold tasks to determine the hand capabilities involved in the tasks and how to simulate them. A syntax is defined to describe hand tasks and anthropomorphic postures. The action manifold is defined to be used as a. functional reference to evaluate artificial hands' performance.A method to evaluate anthropomorphic postures using Fuzzy logic and a method to evaluate anthropomorphic grasping abilities are proposed and applied on models of the human hand and the InMoov robot hand. The results show the methods' ability to detect successful postures and grasps. Future work towards a full implementation of the framework is suggested

Robotics of human movements

The construction of robotic systems that can move the way humans do, with respect to agility, stability and precision, is a necessary prerequisite for the successful integration of robotic systems in human environments. We explain human-centered views on robotics, based on the three basic ingredients (1) actuation; (2) sensing; and (3) control, and formulate detailed examples thereof

Adaptive Control of Arm Movement based on Cerebellar Model

This study is an attempt to take advantage of a cerebellar model to control a biomimetic arm. Aware that a variety of cerebellar models with different levels of details has been developed, we focused on a high-level model called MOSAIC. This model is thought to be able to describe the cerebellar functionality without getting into the details of the neural circuitry. To understand where this model exactly fits, we glanced over the biology of the cerebellum and a few alternative models. Certainly, the arm control loop is composed of other components. We reviewed those elements with emphasis on modeling for our simulation. Among these models, the arm and the muscle system received the most attention. The musculoskeletal model tested independently and by means of optimization techniques, a human-like control of arm through muscle activations achieved. We have discussed how MOSAIC can solve a control problem and what drawbacks it has. Consequently, toward making a practical use of MOSAIC model, several ideas developed and tested. In this process, we borrowed concepts and methods from the control theory. Specifically, known schemes of adaptive control of a manipulator, linearization and approximation were utilized. Our final experiment dealt with a modified/adjusted MOSAIC model to adaptively control the arm. We call this model ORF-MOSAIC (Organized by Receptive Fields MOdular Selection And Identification for Control). With as few as 16 modules, we were able to control the arm in a workspace of 30 x 30 cm. The system was able to adapt to an external field as well as handling new objects despite delays. The discussion section suggests that there are similarities between microzones in the cerebellum and the modules of this new model

Implementation of Industrial Internet of Things and Cyber-Physical Systems in SMEs for Distributed and Service-Oriented Control

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Modelling and Interactional Control of a Multi-fingered Robotic Hand for Grasping and Manipulation.

PhDIn this thesis, the synthesis of a grasping and manipulation controller of the Barrett hand, which is an archetypal example of a multi-fingered robotic hand, is investigated in some detail. This synthesis involves not only the dynamic modelling of the robotic hand but also the control of the joint and workspace dynamics as well as the interaction of the hand with object it is grasping and the environment it is operating in. Grasping and manipulation of an object by a robotic hand is always challenging due to the uncertainties, associated with non-linearities of the robot dynamics, unknown location and stiffness parameters of the objects which are not structured in any sense and unknown contact mechanics during the interaction of the hand’s fingers and the object. To address these challenges, the fundamental task is to establish the mathematical model of the robot hand, model the body dynamics of the object and establish the contact mechanics between the hand and the object. A Lagrangian based mathematical model of the Barrett hand is developed for controller implementation. A physical SimMechanics based model of the Barrett hand is also developed in MATLAB/Simulink environment. A computed torque controller and an adaptive sliding model controller are designed for the hand and their performance is assessed both in the joint space and in the workspace. Stability analysis of the controllers are carried out before developing the control laws. The higher order sliding model controllers are developed for the position control assuming that the uncertainties are in place. Also, this controllers enhance the performance by reducing chattering of the control torques applied to the robot hand. A contact model is developed for the Barrett hand as its fingers grasp the object in the operating environment. The contact forces during the simulation of the interaction of the fingers with the object were monitored, for objects with different stiffness values. Position and force based impedance controllers are developed to optimise the contact force. To deal with the unknown stiffness of the environment, adaptation is implemented by identifying the impedance. An evolutionary algorithm is also used to estimate the desired impedance parameters of the dynamics of the coupled robot and compliant object. A Newton-Euler based model is developed for the rigid object body. A grasp map and a hand Jacobian are defined for the Barrett hand grasping an object. A fixed contact model with friction is considered for the grasping and the manipulation control. The compliant dynamics of Barrett hand and object is developed and the control problem is defined in terms of the contact force. An adaptive control framework is developed and implemented for different grasps and manipulation trajectories of the Barrett hand. The adaptive controller is developed in two stages: first, the unknown robot and object dynamics are estimated and second, the contact force is computed from the estimated dynamics. The stability of the controllers is ensured by applying Lyapunov’s direct method

Neuro-fuzzy modelling and control of robotic manipulators

The work reported in this thesis aims to design and develop a new neuro-fuzzy control system for robotic manipulators using Machine Learning Techniques, Fuzzy Logic Controllers, and Fuzzy Neural Networks. The main idea is to integrate these intelligent techniques to develop an adaptive position controller for robotic manipulators. This will finally lead to utilising one or two coordinated manipulators to perform upper-limb rehabilitation. The main target is to benefit from these intelligent techniques in a systematic way that leads to an efficient control and coordination system. The suggested control system possesses self-learning features so that it can maintain acceptable performance in the presence of uncertain loads. Simulation and modelling stages were performed using dynamical virtual reality programs to demonstrate the ideas of the control and coordination techniques. The first part of the thesis focuses on the development of neuro-fuzzy models that meet the above requirement of mimicking both kinematics and dynamics behaviour of the manipulator. For this purpose, an initial stage for data collection from the motion of the manipulator along random trajectories was performed. These data were then compacted with the help of inductive learning techniques into two sets of if-then rules that form approximation for both of the inverse kinematics and inverse dynamics of the manipulator. These rules were then used in fuzzy neural networks with differentiation characteristics to achieve online tuning of the network adjustable parameters. The second part of the thesis introduces the proposed adaptive neuro-fuzzy joint-based controller. To achieve this target, a feedback Fuzzy-Proportional-Integral-Derivative incremental controller was developed. This controller was then applied as a joint servo-controller for each robot link in addition to the main neuro-fuzzy feedforward controller used to compensate for the dynamics interactions between robot links. A feedback error learning scheme was applied to tune the feedforward neuro-fuzzy controller online using the error back-propagation algorithm. The third part of the thesis presents a neuro-fuzzy Cartesian internal model control system for robotic manipulators. The neuro-fuzzy inverse kinematics model of the manipulator was used in addition to the joint-based controller proposed and the forward mathematical model of the manipulator in an adaptive internal model controller structure. Feedback-error learning scheme was extended to tune both of the joint-based neuro-fuzzy controller and the neuro-fuzzy internal model controller online. The fourth part of the thesis suggests a simple fuzzy hysteresis coordination scheme for two position-controlled robot manipulators. The coordination scheme is based on maintaining certain kinematic relationships between the two manipulators using reference motion synchronisation without explicitly involving the hybrid position/force control or modifying the existing controller structure for either of the manipulators. The key to the success of the new method is to ensure that each manipulator is capable of tracking its own desired trajectory using its own position controller, while synchronizing its motion with the other manipulator motion so that the differential position error between the two manipulators is reduced to zero or kept within acceptable limits. A simplified test-bench emulating upper-limb rehabilitation was used to test the proposed coordination technique experimentally

Learning Algorithm Design for Human-Robot Skill Transfer

In this research, we develop an intelligent learning scheme for performing human-robot skills transfer. Techniques adopted in the scheme include the Dynamic Movement Prim- itive (DMP) method with Dynamic Time Warping (DTW), Gaussian Mixture Model (G- MM) with Gaussian Mixture Regression (GMR) and the Radical Basis Function Neural Networks (RBFNNs). A series of experiments are conducted on a Baxter robot, a NAO robot and a KUKA iiwa robot to verify the effectiveness of the proposed design.During the design of the intelligent learning scheme, an online tracking system is de- veloped to control the arm and head movement of the NAO robot using a Kinect sensor. The NAO robot is a humanoid robot with 5 degrees of freedom (DOF) for each arm. The joint motions of the operator’s head and arm are captured by a Kinect V2 sensor, and this information is then transferred into the workspace via the forward and inverse kinematics. In addition, to improve the tracking performance, a Kalman filter is further employed to fuse motion signals from the operator sensed by the Kinect V2 sensor and a pair of MYO armbands, so as to teleoperate the Baxter robot. In this regard, a new strategy is developed using the vector approach to accomplish a specific motion capture task. For instance, the arm motion of the operator is captured by a Kinect sensor and programmed through a processing software. Two MYO armbands with embedded inertial measurement units are worn by the operator to aid the robots in detecting and replicating the operator’s arm movements. For this purpose, the armbands help to recognize and calculate the precise velocity of motion of the operator’s arm. Additionally, a neural network based adaptive controller is designed and implemented on the Baxter robot to illustrate the validation forthe teleoperation of the Baxter robot.Subsequently, an enhanced teaching interface has been developed for the robot using DMP and GMR. Motion signals are collected from a human demonstrator via the Kinect v2 sensor, and the data is sent to a remote PC for teleoperating the Baxter robot. At this stage, the DMP is utilized to model and generalize the movements. In order to learn from multiple demonstrations, DTW is used for the preprocessing of the data recorded on the robot platform, and GMM is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. Next, we apply the GMR algorithm to generate a synthesized trajectory to minimize position errors in the three dimensional (3D) space. This approach has been tested by performing tasks on a KUKA iiwa and a Baxter robot, respectively.Finally, an optimized DMP is added to the teaching interface. A character recombination technology based on DMP segmentation that uses verbal command has also been developed and incorporated in a Baxter robot platform. To imitate the recorded motion signals produced by the demonstrator, the operator trains the Baxter robot by physically guiding it to complete the given task. This is repeated five times, and the generated training data set is utilized via the playback system. Subsequently, the DTW is employed to preprocess the experimental data. For modelling and overall movement control, DMP is chosen. The GMM is used to generate multiple patterns after implementing the teaching process. Next, we employ the GMR algorithm to reduce position errors in the 3D space after a synthesized trajectory has been generated. The Baxter robot, remotely controlled by the user datagram protocol (UDP) in a PC, records and reproduces every trajectory. Additionally, Dragon Natural Speaking software is adopted to transcribe the voice data. This proposed approach has been verified by enabling the Baxter robot to perform a writing task of drawing robot has been taught to write only one character

Design of a shape memory alloy actuator for soft wearable robots

Soft robotics represents a paradigm shift in the design of conventional robots; while the latter are designed as monolithic structures, made of rigid materials and normally composed of several stiff joints, the design of soft robots is based on the use of deformable materials such as polymers, fluids or gels, resulting in a biomimetic design that replicates the behavior of organic tissues. The introduction of this design philosophy into the field of wearable robots has transformed them from rigid and cumbersome devices into something we could call exo-suits or exo-musculatures: motorized, lightweight and comfortable clothing-like devices. If one thinks of the ideal soft wearable robot (exoskeleton) as a piece of clothing in which the actuation system is fully integrated into its fabrics, we consider that that existing technologies currently used in the design of these devices do not fully satisfy this premise. Ultimately, these actuation systems are based on conventional technologies such as DC motors or pneumatic actuators, which due to their volume and weight, prevent a seamless integration into the structure of the soft exoskeleton. The aim of this thesis is, therefore, to design of an actuator that represents an alternative to the technologies currently used in the field of soft wearable robotics, after having determined the need for an actuator for soft exoskeletons that is compact, flexible and lightweight, while also being able to produce the force required to move the limbs of a human user. Since conventional actuation technologies do not allow the design of an actuator with the required characteristics, the proposed actuator design has been based on so-called emerging actuation technologies, more specifically, on shape memory alloys (SMA). The mechanical design of the actuator is based on the Bowden transmission system. The SMA wire used as the transducer of the actuator has been routed into a flexible sheath, which, in addition to being easily adaptable to the user's body, increases the actuation bandwidth by reducing the cooling time of the SMA element by 30 %. At its nominal operating regime, the actuator provides an output displacement of 24 mm and generates a force of 64 N. Along with the actuator, a thermomechanical model of its SMA transducer has been developed to simulate its complex behavior. The developed model is a useful tool in the design process of future SMA-based applications, accelerating development ix time and reducing costs. The model shows very few discrepancies with respect to the behavior of a real wire. In addition, the model simulates characteristic phenomena of these alloys such as thermal hysteresis, including internal hysteresis loops and returnpoint memory, the dependence between transformation temperatures and applied force, or the effects of latent heat of transformation on the wire heating and cooling processes. To control the actuator, the use of a non-linear control technique called four-term bilinear proportional-integral-derivative controller (BPID) is proposed. The BPID controller compensates the non-linear behavior of the actuator caused by the thermal hysteresis of the SMA. Compared to the operation of two other implemented controllers, the BPID controller offers a very stable and robust performance, minimizing steady-state errors and without the appearance of limit cycles or other effects associated with the control of these alloys. To demonstrate that the proposed actuator together with the BPID controller are a valid solution for implementing the actuation system of a soft exoskeleton, both developments have been integrated into a real soft hand exoskeleton, designed to provide force assistance to astronauts. In this case, in addition to using the BPID controller to control the position of the actuators, it has been applied to the control of the assistive force provided by the exoskeleton. Through a simple mechanical multiplication mechanism, the actuator generates a linear displacement of 54 mm and a force of 31 N, thus fulfilling the design requirements imposed by the application of the exoskeleton. Regarding the control of the device, the BPID controller is a valid control technique to control both the position and the force of a soft exoskeleton using an actuation system based on the actuator proposed in this thesis.La robótica flexible (soft robotics) ha supuesto un cambio de paradigma en el diseño de robots convencionales; mientras que estos consisten en estructuras monolíticas, hechas de materiales duros y normalmente compuestas de varias articulaciones rígidas, el diseño de los robots flexibles se basa en el uso de materiales deformables como polímeros, fluidos o geles, resultando en un diseño biomimético que replica el comportamiento de los tejidos orgánicos. La introducción de esta filosofía de diseño en el campo de los robots vestibles (wearable robots) ha hecho que estos pasen de ser dispositivos rígidos y pesados a ser algo que podríamos llamar exo-trajes o exo-musculaturas: prendas de vestir motorizadas, ligeras y cómodas. Si se piensa en el robot vestible (exoesqueleto) flexible ideal como una prenda de vestir en la que el sistema de actuación está totalmente integrado en sus tejidos, consideramos que las tecnologías existentes que se utilizan actualmente en el diseño de estos dispositivos no satisfacen plenamente esta premisa. En última instancia, estos sistemas de actuaci on se basan en tecnologías convencionales como los motores de corriente continua o los actuadores neumáticos, que debido a su volumen y peso, hacen imposible una integraci on completa en la estructura del exoesqueleto flexible. El objetivo de esta tesis es, por tanto, el diseño de un actuador que suponga una alternativa a las tecnologias actualmente utilizadas en el campo de los exoesqueletos flexibles, tras haber determinado la necesidad de un actuador para estos dispositivos que sea compacto, flexible y ligero, y que al mismo tiempo sea capaz de producir la fuerza necesaria para mover las extremidades de un usuario humano. Dado que las tecnologías de actuación convencionales no permiten diseñar un actuador de las características necesarias, se ha optado por basar el diseño del actuador propuesto en las llamadas tecnologías de actuación emergentes, en concreto, en las aleaciones con memoria de forma (SMA). El diseño mecánico del actuador está basado en el sistema de transmisión Bowden. El hilo de SMA usado como transductor del actuador se ha introducido en una funda flexible que, además de adaptarse facilmente al cuerpo del usuario, aumenta el ancho de banda de actuación al reducir un 30 % el tiempo de enfriamiento del elemento SMA. En su régimen nominal de operaci on, el actuador proporciona un desplazamiento de salida de 24 mm y genera una fuerza de 64 N. Además del actuador, se ha desarrollado un modelo termomecánico de su transductor SMA que permite simular su complejo comportamiento. El modelo desarrollado es una herramienta útil en el proceso de diseño de futuras aplicaciones basadas en SMA, acelerando el tiempo de desarrollo y reduciendo costes. El modelo muestra muy pocas discrepancias con respecto al comportamiento de un hilo real. Además, es capaz de simular fenómenos característicos de estas aleaciones como la histéresis térmica, incluyendo los bucles internos de histéresis y la memoria de puntos de retorno (return-point memory), la dependencia entre las temperaturas de transformacion y la fuerza aplicada, o los efectos del calor latente de transformación en el calentamiento y el enfriamiento del hilo. Para controlar el actuador, se propone el uso de una t ecnica de control no lineal llamada controlador proporcional-integral-derivativo bilineal de cuatro términos (BPID). El controlador BPID compensa el comportamiento no lineal del actuador causado por la histéresis térmica del SMA. Comparado con el funcionamiento de otros dos controladores implementados, el controlador BPID ofrece un rendimiento muy estable y robusto, minimizando el error de estado estacionario y sin la aparición de ciclos límite u otros efectos asociados al control de estas aleaciones. Para demostrar que el actuador propuesto junto con el controlador BPID son una soluci on válida para implementar el sistema de actuación de un exoesqueleto flexible, se han integrado ambos desarrollos en un exoesqueleto flexible de mano real, diseñado para proporcionar asistencia de fuerza a astronautas. En este caso, además de utilizar el controlador BPID para controlar la posición de los actuadores, se ha aplicado al control de la fuerza proporcionada por el exoesqueleto. Mediante un simple mecanismo de multiplicación mecánica, el actuador genera un desplazamiento lineal de 54 mm y una fuerza de 31 N, cumpliendo así con los requisitos de diseño impuestos por la aplicación del exoesqueleto. Respecto al control del dispositivo, el controlador BPID es una técnica de control válida para controlar tanto la posición como la fuerza de un exoesqueleto flexible que use un sistema de actuación basado en el actuador propuesto en esta tesis.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Fabio Bonsignorio.- Secretario: Concepción Alicia Monje Micharet.- Vocal: Elena García Armad

A Soft Multiple-Degree of Freedom Load Cell Based on The Hall Effect

The goal of this thesis is to develop a soft multiple-degree-of-freedom (multi-DOF) load cell that is robust and light weight for use in robotics applications to sense three axes of force and a single axis of torque. The displacement of the magnet within the elastomer changes the magnetic flux density which is sensed by two 3-axis Hall effect sensors. Experimental measurements of magnetic flux density within the area of interest were used to formulate analytic expressions that relate magnet field strength to the position of the magnet. The displacement and orientation measurement and the material properties of the elastomer are used to calibrate and calculate the applied load. The ability to measure 3-DOF force and axial torque was evaluated with combined loading applied by a robotic arm (KUKA, LBR r820 iiwa). The decoupled results show the 4-DOF load cell was able to distinguish 3-axis force and 1-axis torque with 6.9% averaged error for normal force, 4.3% and 2.6% for shear force in the X and Y axis and 8.6% for the torque. The results show good accuracy for a soft multi-axis sensor that would be applicable in many robotic applications where high accuracy is not required