Stochastic recruitment strategies for controlling artificial muscles

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 171-176).This thesis presents a new architecture for controlling active material actuators inspired by biological motor recruitment. An active material is broken down into many small fibers and grouped together to form one large actuator. Each of these fibers is held in a binary state, either relaxed or contracted, using a small local controller which responds to a broadcast input signal from a central controller. The output force and displacement of the actuator is a function of the number of contracted fibers at any point in time. This architecture enables the creation of large-scale, controllable actuators from highly non-linear active materials. The key innovation enabling the central controller to coordinate the behavior of very many small identical units is to randomize the behavior of each unit. This thesis explains how a collection of active material motor units responding in a random, uncorrelated fashion to broadcast commands will exhibit a predictable response that can be stabilized with feedback control and observed using a Kalman filter. Various control strategies will be presented and discussed, including open-loop plant behavior, linear feedback, optimal control, and model-based look-ahead control. Performance metrics such as accuracy and convergence time will be analyzed using dynamic programming and other control techniques. Parallels will also be discussed between this control problem and similar control problems in the field of swarm robotics.(cont.) The stochastic, recruitment-like actuator architecture is demonstrated in shape memory alloy actuators, each composed of 60 individual elements, having a displacement of over 20 mm and a peak force of over 100 N. Control of displacement, isometric force and stiffness are demonstrated using the observer-controller framework. Two actuators are used in an antagonistic fashion to control the stiffness and position of a 1-DOF arm joint.by Lael Ulam Odhner.Sc.D

Model-based Design Framework for Shape Memory Alloy Wire Actuation Devices.

While Shape Memory Alloys (SMAs) have exceptional actuation characteristics such as high energy density, silent operation, flexible packaging, etc., they have not found widespread use in commercial applications because of the significant learning curve required of engineers before they are capable of designing actuation devices using this unique material. An SMA actuation device design framework consisting of grammar, design methods, and design process enables engineers of different backgrounds to make efficient and appropriate design decisions in different stages of the design process. A reference SMA actuation device structure built on a generalized actuation device hierarchical structure using the actuation device grammar works as a reference structure to identify and populate device design options, and to model and analyze the device actuation performance as well as to enlighten non-expert engineers about the essential elements of SMA actuation devices. Design methods consisting of modular modeling, model aggregation and performance prediction, and visualization approaches support design decisions to serve diverse stakeholders of actuation device design by exposing the effects of individual device elements not only for SMA actuation devices, but also for a wide range of actuation devices. A multi-stage design process is formalized to help engineers create a detailed design including a three-step decoupled equilibrium design procedure which prevents potential iteration by decoupling the force and deflection of actuation output behavior, and hides the complexity of material and SMA architectural models from engineers while still exposing the impact of design parameters. The design framework makes SMA design knowledge more accessible to engineers with different levels of expertise and roles in device development by systematically organizing and presenting the device grammar, design methods, and design process. A design tool software platform based on the framework enables the creation of computer-aided design tools to support a variety of design tasks, which were demonstrated in two use case examples. By having the SMA actuation device design framework, the acceptance of the SMA actuation technology into both research and commercial applications can be increased to utilize promising SMA actuation benefits, and the device development cycle leading to these applications can be streamlined.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120684/1/wonhekim_1.pd

Designing a New Tactile Display Technology and its Disability Interactions

People with visual impairments have a strong desire for a refreshable tactile interface that can provide immediate access to full page of Braille and tactile graphics. Regrettably, existing devices come at a considerable expense and remain out of reach for many. The exorbitant costs associated with current tactile displays stem from their intricate design and the multitude of components needed for their construction. This underscores the pressing need for technological innovation that can enhance tactile displays, making them more accessible and available to individuals with visual impairments. This research thesis delves into the development of a novel tactile display technology known as Tacilia. This technology's necessity and prerequisites are informed by in-depth qualitative engagements with students who have visual impairments, alongside a systematic analysis of the prevailing architectures underpinning existing tactile display technologies. The evolution of Tacilia unfolds through iterative processes encompassing conceptualisation, prototyping, and evaluation. With Tacilia, three distinct products and interactive experiences are explored, empowering individuals to manually draw tactile graphics, generate digitally designed media through printing, and display these creations on a dynamic pin array display. This innovation underscores Tacilia's capability to streamline the creation of refreshable tactile displays, rendering them more fitting, usable, and economically viable for people with visual impairments

Hierarchical fibrous structures for muscle-inspired soft-actuators:A review

Inspired by Nature, one of the most ambitious challenge in soft robotics is to design actuators capable of reaching performances comparable to the skeletal muscles. Considering the perfectly balanced features of natural muscular tissue in terms of linear contraction, force‐to‐weight ratio, scalability and morphology, scientists have been working for many years on mimicking this structure. Focusing on the biomimicry, this review investigates the state‐of‐the‐art of synthetic fibrous, muscle‐inspired actuators that, aiming to enhance their mechanical performances, are hierarchically designed from the nanoscale up to the macroscale. In particular, this review focuses on those hierarchical fibrous actuators that enhance their biomimicry employing a linear contraction strategy, closely resembling the skeletal muscles actuation system. The literature analysis shows that bioinspired artificial muscles, developed up to now, only in part comply with skeletal ones. The manipulation and control of the matter at the nanoscale allows to realize ordered structures, such as nanofibers, used as elemental actuators characterized by high strains but moderate force levels. Moreover, it can be foreseen that scaling up the nanostructured materials into micro‐ and macroscale hierarchical structures, it is possible to realize linear actuators characterized by suitable levels of force and displacement

Dynamic Cellular Actuator Arrays and Expanded Fingerprint Method for Dynamic Modeling

Copyright © ElsevierDOI: http://dx.doi.org/10.1016/j.robot.2013.06.013A key step to understanding and producing natural motion is creating a physical, well understood actuator with a dynamic model resembling biological muscle. This actuator can then serve as the basis for building viable, full-strength, and safe muscles for disabled patients, rehabilitation, human force amplification, telerobotics, and humanoid robotic systems. This paper presents a cell-based flexible actuator modeling methodology and the General Fingerprint Method for systematically and efficiently calculating the actuators’ respective dynamic equations of motion. The cellular actuator arrays combine many flexible ‘cells’ in complex and varied topologies for combined large-scale motion. The cells can have varied internal dynamic models and common actuators such as piezoelectric, SMA, linear motor, and pneumatic technologies can fit the model by adding a flexible element in series with the actuator. The topology of the cellular actuator array lends it many of its properties allowing the final muscle to be catered to particular applications. The General Fingerprint Method allows for fast recalculation for different and/or changing structures and internal dynamics, and provides an intuitive base for future controls work. This paper also presents two physical SMA based cellular actuator arrays which validate the presented theory and give a basis for future development

Microfluidics and Neural Interfaces Development for the Safe Direct Current Stimulator

Safety of commercial neural implants fundamentally limits its working to the use of charge-balanced, biphasic pulses to interact with target neurons using metal electrodes. Short biphasic pulses are used to avoid toxic electrochemical reactions at the electrode-tissue interfaces. Biphasic pulses are effective at exciting neurons, but quite limited in inhibiting their activity. In contrast, direct current can both excite and inhibit neurons, however it leads to the formation of harmful, Faradaic reactions at the metal electrode/tissue interface. To address this challenge of safety over chronic use, we are developing the Safe Direct Current Stimulator (SDCS) technology, that generates an ionic direct current (iDC) from a biphasic input signal using a network of microfluidic channels and mechanical valves. This rectified iDC is applied to the target neural tissue through an ionically conductive neural interface. A key enabler towards transforming the SDCS concept from a benchtop design to an implantable neural prosthesis is the design of a miniature valve. Several valve architectures and actuation mechanism were studied for the development of the microfluidics in SDCS technology, before settling on the plunger-membrane microvalve design. This thesis characterizes a miniature polydimethylsiloxane (PDMS) based elastomeric normally closed (NC) mechanical valve actuated using a shape-memory alloy (SMA) wire through distinct tests and examines its current capability for iDC delivery. The analysis of the test outputs confirmed the feasibility of using this design for rectifying the charge-balanced alternating current (AC) into iDC. As metal electrodes are unsuitable for delivering iDC to the neural tissue safely, an ionic conductive neural lead is built. These gel-based, PDMS electrodes should be designed within the acceptable pressure limits that a nerve can handle safely. Preliminary experiments were conducted to verify the design and conductivity of the lead. While the results suggest that the lead design maintains the pressure below the maximum limit, its high impedance raises concerns. Although this thesis forms a basis for development of the SDCS device, further experimentation and progress is required for a reliable, safe, chronic, and fully functional device

Medical robots with potential applications in participatory and opportunistic remote sensing: A review

Among numerous applications of medical robotics, this paper concentrates on the design, optimal use and maintenance of the related technologies in the context of healthcare, rehabilitation and assistive robotics, and provides a comprehensive review of the latest advancements in the foregoing field of science and technology, while extensively dealing with the possible applications of participatory and opportunistic mobile sensing in the aforementioned domains. The main motivation for the latter choice is the variety of such applications in the settings having partial contributions to functionalities such as artery, radiosurgery, neurosurgery and vascular intervention. From a broad perspective, the aforementioned applications can be realized via various strategies and devices benefiting from detachable drives, intelligent robots, human-centric sensing and computing, miniature and micro-robots. Throughout the paper tens of subjects, including sensor-fusion, kinematic, dynamic and 3D tissue models are discussed based on the existing literature on the state-of-the-art technologies. In addition, from a managerial perspective, topics such as safety monitoring, security, privacy and evolutionary optimization of the operational efficiency are reviewed

Pattern recognition-based real-time myoelectric control for anthropomorphic robotic systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronics at Massey University, Manawatū, New Zealand

All copyrighted Figures have been removed but may be accessed via their source cited in their respective captions.Advanced human-computer interaction (HCI) or human-machine interaction (HMI) aims to help humans interact with computers smartly. Biosignal-based technology is one of the most promising approaches in developing intelligent HCI systems. As a means of convenient and non-invasive biosignal-based intelligent control, myoelectric control identifies human movement intentions from electromyogram (EMG) signals recorded on muscles to realise intelligent control of robotic systems. Although the history of myoelectric control research has been more than half a century, commercial myoelectric-controlled devices are still mostly based on those early threshold-based methods. The emerging pattern recognition-based myoelectric control has remained an active research topic in laboratories because of insufficient reliability and robustness. This research focuses on pattern recognition-based myoelectric control. Up to now, most of effort in pattern recognition-based myoelectric control research has been invested in improving EMG pattern classification accuracy. However, high classification accuracy cannot directly lead to high controllability and usability for EMG-driven systems. This suggests that a complete system that is composed of relevant modules, including EMG acquisition, pattern recognition-based gesture discrimination, output equipment and its controller, is desirable and helpful as a developing and validating platform that is able to closely emulate real-world situations to promote research in myoelectric control. This research aims at investigating feasible and effective EMG signal processing and pattern recognition methods to extract useful information contained in EMG signals to establish an intelligent, compact and economical biosignal-based robotic control system. The research work includes in-depth study on existing pattern recognition-based methodologies, investigation on effective EMG signal capturing and data processing, EMG-based control system development, and anthropomorphic robotic hand design. The contributions of this research are mainly in following three aspects: Developed precision electronic surface EMG (sEMG) acquisition methods that are able to collect high quality sEMG signals. The first method was designed in a single-ended signalling manner by using monolithic instrumentation amplifiers to determine and evaluate the analog sEMG signal processing chain architecture and circuit parameters. This method was then evolved into a fully differential analog sEMG detection and collection method that uses common commercial electronic components to implement all analog sEMG amplification and filtering stages in a fully differential way. The proposed fully differential sEMG detection and collection method is capable of offering a higher signal-to-noise ratio in noisy environments than the single-ended method by making full use of inherent common-mode noise rejection capability of balanced signalling. To the best of my knowledge, the literature study has not found similar methods that implement the entire analog sEMG amplification and filtering chain in a fully differential way by using common commercial electronic components. Investigated and developed a reliable EMG pattern recognition-based real-time gesture discrimination approach. Necessary functional modules for real-time gesture discrimination were identified and implemented using appropriate algorithms. Special attention was paid to the investigation and comparison of representative features and classifiers for improving accuracy and robustness. A novel EMG feature set was proposed to improve the performance of EMG pattern recognition. Designed an anthropomorphic robotic hand construction methodology for myoelectric control validation on a physical platform similar to in real-world situations. The natural anatomical structure of the human hand was imitated to kinematically model the robotic hand. The proposed robotic hand is a highly underactuated mechanism, featuring 14 degrees of freedom and three degrees of actuation. This research carried out an in-depth investigation into EMG data acquisition and EMG signal pattern recognition. A series of experiments were conducted in EMG signal processing and system development. The final myoelectric-controlled robotic hand system and the system testing confirmed the effectiveness of the proposed methods for surface EMG acquisition and human hand gesture discrimination. To verify and demonstrate the proposed myoelectric control system, real-time tests were conducted onto the anthropomorphic prototype robotic hand. Currently, the system is able to identify five patterns in real time, including hand open, hand close, wrist flexion, wrist extension and the rest state. With more motion patterns added in, this system has the potential to identify more hand movements. The research has generated a few journal and international conference publications