Ahsan I. Nawroj

Simple, scalable active cells for articulated robot structures

The proposed research effort explores the development of active cells - simple contractile electromechanical units that can be used as the material basis for larger articulable structures. Each cell, which might be considered a “muscle unit”, consists of a contractile Nitinol SMA core with conductive terminals. Large numbers of these cells might be combined and externally powered to change phase, contracting to either articulate with a large strain or increase the stiffness of the ensemble, depending on the cell design. Unlike traditional work in modular robotics, the approach presented here focuses on cells that have a simplistic design and function, are inexpensive to fabricate, and are eventually scalable to sub-millimeter sizes, working towards our vision of robot structures that can be custom-fabricated from large numbers of general cell units, similar to biological structures.

Design of mesoscale active cells for networked, compliant robotic structures

We present the design of simple, centimeter-scale modular actuation units (“Active Cells”) and passive compliant nodes that are electromechanically networked to create macroscopically deformable Modular Active Cell-based Structures (MACROs). Each Active Cell is a single degree-of-freedom linear actuator (a “muscle unit”), consisting of fiberglass end-pieces connecting two strands of Nitinol shape-memory alloy and a passive biasing spring. The Nitinol strands are coiled into a tight spring to increase deformations when activated through resistive heating. In-depth examination of the optimization of Nitinol coils with an antagonistic spring is presented, resulting in large repeatable axial cell strains of up to 25%. The design of these cellular muscle units to obtain maximal repeatable stroke is presented, allowing for the construction of larger networks of cells (MACRO modules, akin to a biological “tissue”) that can be customized to a target application. Finally, experimental demonstration of the construction and actuation of some simple MACRO modules is described.

Toward Modular Active-Cell Robots (MACROs): SMA Cell Design and Modeling of Compliant, Articulated Meshes

In this paper, we present the design of a shape-memory-alloy (SMA)-based compliant linear actuator [active cell (AC)] and the use of these in designing and modeling articulated meshes, which form the mechanical subsystem of a class of proposed modular active-cell robots (MACROs). The ACs are capable of undergoing ∼25% strain and groups of cells are connected via passively compliant nodes to produce articulated mesh networks. The deformation of compliant meshes of ACs is modeled by scale-invariant parametric equations derived from the physics of SMA deformations and a reduced-order model of the cells. Parameters of the implemented system were used to develop a simulation platform that predicts the mechanical deformation of the networked robot given electrical inputs at arbitrary nodes of the network. We provide results of several experimental trials used to validate and establish the accuracy of this deformation model. The error in predicting deformations in small meshes is shown to be under 10% over both time-varying inputs and at steady states.

Electrically conductive bulk composites through a contact-connected aggregate.

This paper introduces a concept that allows the creation of low-resistance composites using a network of compliant conductive aggregate units, connected through contact, embedded within the composite. Due to the straight-forward fabrication method of the aggregate, conductive composites can be created in nearly arbitrary shapes and sizes, with a lower bound near the length scale of the conductive cell used in the aggregate. The described instantiation involves aggregate cells that are approximately spherical copper coils-of-coils within a polymeric matrix, but the concept can be implemented with a wide range of conductor elements, cell geometries, and matrix materials due to its lack of reliance on specific material chemistries. The aggregate cell network provides a conductive pathway that can have orders of magnitude lower resistance than that of the matrix material - from 1012 ohm-cm (approx.) for pure silicone rubber to as low as 1 ohm-cm for the silicone/copper composite at room temperature for the presented example. After describing the basic concept and key factors involved in its success, three methods of implementing the aggregate into a matrix are then addressed – unjammed packing, jammed packing, and pre-stressed jammed packing – with an analysis of the tradeoffs between increased stiffness and improved resistivity.

Shape Control of Compliant, Articulated Meshes: Towards Modular Active-Cell Robots (MACROs)

In this letter, we explore the shape control of compliant, articulated meshes created from shape memory alloy (SMA)-based linear actuators (Active Cells). These compliant meshes form the mechanical subsystem of a class of proposed modular active-cell robots (MACROs). Our “Active Cells” are centimeter-scale SMA actuators capable of ∼25% linear strain. The deformation of MACRO meshes in response to current inputs at the passive nodes are modeled and validated for accuracy in prior work. In this letter, we investigate an efficient and scalable control policy that allows us a given MACRO to be electrically-driven to achieve a specified shape. Validation experiments on a range of MACRO simulations establish a high degree of accuracy and repeatability of the controller. The control strategy is shown to be efficient and robust to variations in start- and target-shapes, and in mesh complexity.

Design of a Bulk Conductive Polymer Using Embedded Macroscopic Copper Cells

This paper introduces a technique of inducing bulk conductivity in a polymer. The technique uses coiled copper ‘cells’ embedded into a polymer during fabrication which can subsequently create highly redundant series-parallel networks. The preceding body of work aimed to improve the conductivity of non-conducting polymers by embedding particulates (of metal, carbon, etc.) into the polymer, or by altering the polymerization chemistry to incorporate conductive elements. The technique described here keeps the process independent of the specific polymer chosen by not relying on the polymerization chemistry to aid in the incorporation of the cells. The embedding drastically lowers the resistivity of the polymer, from 1012 Ω -cm (approx.) for pure silicone rubber to less than 50 Ω -cm for the composite at room temperature: a drop of 12 orders of magnitude. A secondary consideration of this paper is the mechanical stiffness changes brought about by the embedding of metal inside a flexible polymer. Although the connected network of copper cells allows the rubber to be highly conductive in bulk, the cells are themselves compliant and thus have minimal effect on the stiffness of the cured silicone rubber.Copyright

The organization of digit contact timing during grasping

While the process of hand preshaping during grasping has been studied for over a decade, there is relatively little information regarding the organization of digit contact timing (DCT). This dearth of information may be due to the assumption that DCT while grasping exhibits few regularities or to the difficulty in obtaining information through traditional movement recording techniques. In this study, we employed a novel technique to determine the time of digit contacts with the target object at a high precision rate in normal healthy participants. Our results indicate that, under our task conditions, subjects tend to employ a radial to ulnar pattern of DCT which may be modulated by the shape of the target object. Moreover, a number of parameters, such as the total contact time, the frequency of first contacts by the thumb and index fingers and the number of simultaneous contacts, are affected by the relative complexity of the target object. Our data support the notion that a great deal of information about the object’s physical features is obtained during the early moments of the grasp.

Microprocessor control of an ultrasonic scanning device

A mechanical device has been developed as an ophthalmic ultrasonic scanning probe. The device is composed of an oscillating probe where the ultrasound transducer is mounted, part of which is flexible so the probe can oscillate when energized. Electric current is supplied to two electromagnetic coils acting on two permanent magnets mounted on both sides of the probe. A controller is employed to control the angular position of the probe during operation. Computer simulation, including the mechanical device and the controller, shows promising performance over the entire operation frequency.

An event classifier using EEG signals: An artificial neural network approach

An event classifier has been developed to analyze the collected EEG signals and distinguish between different events. The classifier introduced in this study was based on an artificial neural network model. The training process of the neural network required large amount of sample data and target results. A trained artificial neural network can then predict the outcome of an event based on the information of the corresponding EEG signal. The architecture of the artificial neural network involved hidden layers in addition to the input and output layers, which satisfied the non-linearity of the problem that the classifier was designed to solve. Experiments were conducted to validate this approach by using the classifier to distinguish whether subjects placed their fingers into hot or cold water. Validation results demonstrated the effectiveness of the classifier and its potential application in other fields.

A brain-computer interface for robot navigation

A brain-computer interface (BCI) system has been developed for a user to navigate a robot via “thinking”. The users intent was extracted from the P300 response of the recorded EEG signals using the BCI2000 platform. This paper describes the design, development and testing of this new application: to remotely control the motion of a robot using EEG (electroencephalogram) data collected during real-time trials.