Modeling, identification, and application of multilayer polypyrrole conducting polymer actuators

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references.Experiments were performed using commercially available, self-contained, multilayer polypyrrole (PPy) actuators to develop low-order lumped parameter models of actuator electrical, mechanical, and electromechanical behavior. Experimental data were processed using system identification techniques. Both grey box and black box models were identified. The grey box model consisted of a first order electrical network that was linearly and algebraically coupled to a second order viscoelastic model. The black box model incorporated a third order Box-Jenkins structure and achieved model to data residues comparable to the grey box model. When utilizing validation data, the grey box model showed very good performance for loads in the range of 0.5 to 3 N. Overall, the results of system identification experiments suggested that low order, lumped parameter models were adequate to describe the gross behavior of multilayer actuators. An online identification scheme was developed for monitoring polymer electrical impedance and thereby monitoring the degradation state of an actuator. This identification was performed successfully using recursive least squares and least squares for a discrete impedance model.(cont.) Experimental validation data, spanning more than 5 hours of continuous operation, were collected and analyzed. A final contribution of this research was the application PPy linear actuators to a custom-designed humanoid foot. Four linear conducting polymer actuators were used to obtain multifunctional behavior of the overall foot. Jacobian analysis of stiffness and damping was performed for the design. Simulations illustrated that PPy actuators through the use of appropriate electrical excitation can modulate their stiffness characteristics as a function of time to match a desired force versus length relationship.by Thomas W. Secord.S.M

Doctor of Philosophy

dissertationConducting polymer actuators have shown numerous improvements in mechanical performance over the last couple of decades, but can be better utilized in applications with the ability to adjust to unknown operating conditions, or improved during their lifetime. This work employs the process of sequential growth to initially fabricate polypyrrole-metal coil composite actuators, and then again for further actuator growth during its lifetime of operation. The novel synthesis process was first shown through the use of a custom testing apparatus that could support the sequential growth process by allowing different actuation and synthesis solutions to be controlled in the test cell, as well as facilitate mechanical performance testing. Open-loop testing demonstrated the actuator system performance for multiple growth stages over multiple input frequencies, and was then compared to the parameters identified to fit a simplified model during operation. The simplified model was shown to differentiate from the experimental data, but provided useful optimal growth prediction values with a performance cost evaluation algorithm. The model could predict the optimal growth determined by the experimental data to within one growth stage. Performance was improved by using a proportional-derivative feedback controller where the gains were calculated by the desired response at each growth stage for each sample. The cost performance was performed again with the closed-loop data, but did an inferior job of predicting the optimal amount of growth for each sample compared to the open-loop data. The simplified model accurately tracked the behavior changes through multiple stages of growth. The main contributions of this work include a novel testing apparatus and synthesis method for multiple growth steps, the implementation of a simplified model for tracking and optimal growth stage prediction, and the application of a model-based proportional-derivative feedback controller

Soft Robotics: Design for Simplicity, Performance, and Robustness of Robots for Interaction with Humans.

This thesis deals with the design possibilities concerning the next generation of advanced Robots. Aim of the work is to study, analyse and realise artificial systems that are essentially simple, performing and robust and can live and coexist with humans. The main design guideline followed in doing so is the Soft Robotics Approach, that implies the design of systems with intrinsic mechanical compliance in their architecture. The first part of the thesis addresses design of new soft robotics actuators, or robotic muscles. At the beginning are provided information about what a robotic muscle is and what is needed to realise it. A possible classification of these systems is analysed and some criteria useful for their comparison are explained. After, a set of functional specifications and parameters is identified and defined, to characterise a specific subset of this kind of actuators, called Variable Stiffness Actuators. The selected parameters converge in a data-sheet that easily defines performance and abilities of the robotic system. A complete strategy for the design and realisation of this kind of system is provided, which takes into account their me- chanical morphology and architecture. As consequence of this, some new actuators are developed, validated and employed in the execution of complex experimental tasks. In particular the actuator VSA-Cube and its add-on, a Variable Damper, are developed as the main com- ponents of a robotics low-cost platform, called VSA-CubeBot, that v can be used as an exploratory platform for multi degrees of freedom experiments. Experimental validations and mathematical models of the system employed in multi degrees of freedom tasks (bimanual as- sembly and drawing on an uneven surface), are reported. The second part of the thesis is about the design of multi fingered hands for robots. In this part of the work the Pisa-IIT SoftHand is introduced. It is a novel robot hand prototype designed with the purpose of being as easily usable, robust and simple as an industrial gripper, while exhibiting a level of grasping versatility and an aspect comparable to that of the human hand. In the thesis the main theo- retical tool used to enable such simplification, i.e. the neuroscience– based notion of soft synergies, are briefly reviewed. The approach proposed rests on ideas coming from underactuated hand design. A synthesis method to realize a desired set of soft synergies through the principled design of adaptive underactuated mechanisms, which is called the method of adaptive synergies, is discussed. This ap- proach leads to the design of hands accommodating in principle an arbitrary number of soft synergies, as demonstrated in grasping and manipulation simulations and experiments with a prototype. As a particular instance of application of the method of adaptive syner- gies, the Pisa–IIT SoftHand is then described in detail. The design and implementation of the prototype hand are shown and its effec- tiveness demonstrated through grasping experiments. Finally, control of the Pisa/IIT Hand is considered. Few different control strategies are adopted, including an experimental setup with the use of surface Electromyographic signals

Design and application of a cellular, piezoelectric, artificial muscle actuator for biorobotic systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 219-227).One of the foremost challenges in robotics is the development of muscle-like actuators that have the capability to reproduce the smooth motions observed in animals. Biological muscles have a unique cellular structure that departs from traditional electromechanical actuators in several ways. A muscle consists of a vast number of muscle fibers and, more fundamentally, sarcomeres that act as cellular units or building blocks. A muscle's output force and displacement are the aggregate effect of the individual building blocks. Thus, without using gearing or transmissions, muscles can be tailored to a range of loads, satisfying specific force and displacement requirements. These natural actuators are desirable for biorobotic applications, but many of their characteristics have been difficult to reproduce artificially. This thesis develops and applies a new artificial muscle actuator based on piezoelectric technology. The essential approach is to use a subdivided, cellular architecture inspired by natural muscle. The primary contributions of this work stem from three sequential aims. The first aim is to develop the operating principles and design of the actuator cellular units. The basic operating principle of the actuator involves nested flexural amplifiers applied to piezoelectric stacks thereby creating an output length strain commensurate with natural muscle. The second aim is to further improve performance of the actuator design by imparting tunable stiffness and resonance capabilities. This work demonstrates a previously unavailable level of tunability in both stiffness and resonance. The final aim is to showcase the capabilities of the actuator design by developing an underwater biorobotic fish system that utilizes the actuators for resonance-based locomotion. Each aspect of this thesis is supported by rigorous analysis and functional prototypes that augment broadly applicable design concepts.by Thomas William Secord.Ph.D

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

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Department of Energy EngineeringElectronic skins (e-skins) enabling to detect various mechanical/chemical stimuli and environmental conditions by converting into various electrical and optical signals have attracted much attentions for various fields including wearable electronics, intelligent/medical robotics, healthcare monitoring devices, and haptic interfaces. Conventional e-skins have been widely used for the realization of these applications, however it is still considered that new e-skins with enhanced sensor performances (i.e. sensitivity, flexibility, multifunctionality, etc.) should be developed. In accordance with these demands, two approaches to explore novel functional materials or to modify device architectures have been introduced for enhancing sensor performance and acquiring multifunctional sensing capabilities. Firstly, a synthesis of multifunctional materials combined with conductive fillers (carbon nanotube, graphene oxide) and functional polymer matrix (i.e. ferroelectric polymer, elastomer) can provide the multimodal sensing capability of various stimuli and stretchability. Secondly, controlling design of device structures into various micro/nanostructures enables a significant improvement on sensing capabilities of e-skins with sensitivity and multidirectional force sensing, resulting from structural advantages such as large surface area, effective stress propagation, and anisotropic deformation. Therefore, a demonstration of e-skin combined with the functional composites and uniquely designed microstructures can offer a powerful platform to realize ideal sensor systems for next generation applications such as wearable electronics, healthcare devices, acoustic sensor, and haptic interface devices. In this thesis, we introduce the novel multifunctional and high performance electronic skins combined with various types of composite materials and nature-inspired 3D microstructures. Firstly, Chapter 1 briefly introduces various types of e-skins and the latest research trends of microstructured e-skins and summarizes the key components for their promising application fields. In chapters 2 and 3, mimicked by interlocking system between epidermal and dermal layers in human skin, we demonstrate the piezoresistive e-skins based on CNT/PDMS composite materials with interlocked microdome arrays for great pressure sensitivity and multidirectional force sensing capabilities. In chapter 4, we conduct in-depth study on giant tunneling piezoresistance in interlocking system and investigate systematically on the geometrical effect of microstructures on multidirectional force sensitivity and selectivity in interlocking sensor systems. In chapter 5, we demonstrate the ferroelectric e-skin that can detect and discriminate the static/dynamic touches and temperature inspired by multi-stimuli detection of various mechanoreceptors in human skin. Using the multifunctional sensing capabilities, we demonstrated our e-skin to the temperature-dependent pressure monitoring of artery vessel, high-precision acoustic sound detection, and surface texture recognition of various surfaces. In chapter 6, we demonstrate the linear and wide range pressure sensor with multilayered composite films having interlocked microdomes. In chapter 7, we present a new-concept of e-skin based on mechanochromic polymer and porous structures for overcoming limitations in conventional mechanochromic systems with low mechanochromic performances and limited stretchability. In addition, our mechanochromic e-skins enable the dual-mode detection of static and dynamic forces without any external power. Our e-skins based on functional composites and uniquely designed microstructures can provide a solid platform for next generation eskin in wearable electronics, humanoid robotics, flexible sensors, and wearable medical diagnostic systems.clos

Chapter 34 - Biocompatibility of nanocellulose: Emerging biomedical applications

Nanocellulose already proved to be a highly relevant material for biomedical applications, ensued by its outstanding mechanical properties and, more importantly, its biocompatibility. Nevertheless, despite their previous intensive research, a notable number of emerging applications are still being developed. Interestingly, this drive is not solely based on the nanocellulose features, but also heavily dependent on sustainability. The three core nanocelluloses encompass cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and bacterial nanocellulose (BNC). All these different types of nanocellulose display highly interesting biomedical properties per se, after modification and when used in composite formulations. Novel applications that use nanocellulose includewell-known areas, namely, wound dressings, implants, indwelling medical devices, scaffolds, and novel printed scaffolds. Their cytotoxicity and biocompatibility using recent methodologies are thoroughly analyzed to reinforce their near future applicability. By analyzing the pristine core nanocellulose, none display cytotoxicity. However, CNF has the highest potential to fail long-term biocompatibility since it tends to trigger inflammation. On the other hand, neverdried BNC displays a remarkable biocompatibility. Despite this, all nanocelluloses clearly represent a flag bearer of future superior biomaterials, being elite materials in the urgent replacement of our petrochemical dependence

Prediction of Robot Execution Failures Using Neural Networks

In recent years, the industrial robotic systems are designed with abilities to adapt and to learn in a structured or unstructured environment. They are able to predict and to react to the undesirable and uncontrollable disturbances which frequently interfere in mission accomplishment. In order to prevent system failure and/or unwanted robot behaviour, various techniques have been addressed. In this study, a novel approach based on the neural networks (NNs) is employed for prediction of robot execution failures. The training and testing dataset used in the experiment consists of forces and torques memorized immediately after the real robot failed in assignment execution. Two types of networks are utilized in order to find best prediction method - recurrent NNs and feedforward NNs. Moreover, we investigated 24 neural architectures implemented in Matlab software package. The experimental results confirm that this approach can be successfully applied to the failures prediction problem, and that the NNs outperform other artificial intelligence techniques in this domain. To further validate a novel method, real world experiments are conducted on a Khepera II mobile robot in an indoor structured environment. The obtained results for trajectory tracking problem proved usefulness and the applicability of the proposed solution

Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment

To navigate autonomously in a manufacturing environment Automated Guided Vehicle (AGV) needs the ability to infer its pose. This paper presents the implementation of the Extended Kalman Filter (EKF) coupled with a feedforward neural network for the Visual Simultaneous Localization and Mapping (VSLAM). The neural extended Kalman filter (NEKF) is applied on-line to model error between real and estimated robot motion. Implementation of the NEKF is achieved by using mobile robot, an experimental environment and a simple camera. By introducing neural network into the EKF estimation procedure, the quality of performance can be improved