Browsing by Author "Edward Grant, Committee Chair"

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The Application of MEMS Accelerometers for Accurately Finding Warp Yarn Breaks in Textile Machinery
(2003-07-06) Slusser, Tim; Edward Grant, Committee Chair; George Hodge, Committee Member; Mark White, Committee Member
The textile industry, particularly in the weaving areas, needs sensors to monitor for faults and to aid the automation of warp yarn repair. As MEMS (MicroElectroMechanical Systems) technology advances, sensors and actuators get smaller. MEMS sensors are very powerful and are highly accurate. These sensors are inexpensive and are readily available. Currently, in the textile machinery, drop wires are used to monitor the tension of the warp yarns in the weaving process. These drop wires are abrasive to the warp yarns and can lead to more warp yarn breaks. Therefore, it would be beneficial to develop a system that does not contact the warp yarn in any way, such that extra warp yarns are not broken because of the sensor. This research has led to the development of a sensor system that has no contact with the warp yarn. The main purpose was to show proof of concept for applying a MEMS sensor to the textile machinery, specifically the Jacquard Loom, in order to develop a sensor system having no contact with the warp yarns. A MEMS Accelerometer, available from Analog Devices, was used to monitor the motion of the heddle, whose acceleration properties change based on the presence of the warp yarn. Matlab was used to interpret the data and analyze for broken warp yarns (using recorded data) based on the change in acceleration. Once a warp yarn is determined to have been broken, Matlab would notify the user.
Applying Wide Field of View Retroreflector Technology to Free Space Optical Robotic Communications
(2007-10-04) Alhammadi, Khalid A.; C Frank Abrams, Committee Member; H. Troy Nagle, Committee Member; John F. Muth, Committee Co-Chair; Edward Grant, Committee Chair
Competitive Relative Performance and Fitness Selection for Evolutionary Robotics
(2003-05-21) Nelson, Andrew Lincoln; Edward Grant, Committee Chair; Mark White, Committee Member; Paul Ro, Committee Member; Wesley E Snyder, Committee Member; John Muth, Committee Member
Evolutionary Robotics (ER) is a field of research that applies evolutionary computing methods to the automated design and synthesis of behavioral robotics controllers. In the general case, reinforcement learning (RL) using high-level task performance feedback is applied to the evolution of controllers for autonomous mobile robots. This form of RL learning is required for the evolution of complex and non-trivial behaviors because a direct error-feedback signal is generally not available. Only the high-level behavior or task is known, not the complex sensor-motor signal mappings that will generate that behavior. Most work in the field has used evolutionary neural computing methods. Over the course of the preceding decade, ER research has been largely focused on proof-of-concept experiments. Such work has demonstrated both the evolvablility of neural network controllers and the feasibility of implementation of those evolved controllers on real robots. However, these proof-of-concept results leave important questions unanswered. In particular, no ER work to date has shown that it is possible to evolve complex controllers in the general case. The research described in this work addresses issues relevant to the extension of ER to generalized automated behavioral robotics controller synthesis. In particular, we focus on fitness selection function specification. The case is made that current methods of fitness selection represent the primary factor limiting the further development of ER. We formulate a fitness function that accommodates the Bootstrap Problem during early evolution, but that limits human bias in selection later in evolution. In addition, we apply ER methods to evolve networks that have far more inputs, and are of a much greater complexity than those used in other ER work. We focus on the evolution of robot controllers for the competitive team game Capture the Flag. Games are played in a variety of maze environments. The robots use processed video data requiring 150 or more neural network inputs for sensing of their environment. The evolvable artificial neural network (ANN) controllers are of a general variable-size architecture that allows for arbitrary connectivity. Resulting evolved ANN controllers contain on the order of 5000 weights. The evolved controllers are tested in competitions of 240 games against hand-coded knowledge-based controllers. Results show that evolved controllers are competitive with the knowledge-based controllers and can win a modest majority of games in a large tournament in a challenging world configuration.
Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-objective Genetic Programming
(2004-03-23) Barlow, Gregory John; H. Troy Nagle, Committee Member; Choong K. Oh, Committee Member; Edward Grant, Committee Chair; Mark W. White, Committee Member
Unmanned aerial vehicles (UAVs) have become increasingly popular for many applications, including search and rescue, surveillance, and electronic warfare, but almost all UAVs are controlled remotely by humans. Methods of control must be developed before UAVs can become truly autonomous. While the field of evolutionary robotics (ER) has made strides in using evolutionary computation (EC) to develop controllers for wheeled mobile robots, little attention has been paid to applying EC to UAV control. EC is an attractive method for developing UAV controllers because it allows the human designer to specify the set of high level goals that are to be solved by artificial evolution. In this research, autonomous navigation controllers were developed using multi-objective genetic programming (GP) for fixed wing UAV applications. Four behavioral fitness functions were derived from flight simulations. Multi-objective GP used these fitness functions to evolve controllers that were able to locate an electromagnetic energy source, to navigate the UAV to that source efficiently using on-board sensor measurements, and to circle around the emitter. Controllers were evolved in simulation. To narrow the gap between simulated and real controllers, the simulation environment employed noisy radar signals and a sensor model with realistic inaccuracies. All computations were performed on a 92-processor Beowulf cluster parallel computer. To gauge the success of evolution, baseline fitness values for a successful controller were established by selecting values for a minimally successful controller. Two sets of experiments were performed, the first evolving controllers directly from random initial populations, the second using incremental evolution. In each set of experiments, autonomous navigation controllers were evolved for a variety of radar types. Both the direct evolution and incremental evolution experiments were able to evolve controllers that performed acceptably. However, incremental evolution vastly increased the success rate of incremental evolution over direct evolution. The final incremental evolution experiment on the most complex radar investigated in this research evolved controllers that were able to handle all of the radar types. Evolved UAV controllers were successfully transferred to a wheeled mobile robot. An acoustic array on-board the mobile robot replaced the radar sensor, and a speaker emitting a tone was used as the target. Using the evolved navigation controllers, the mobile robot moved to the speaker and circled around it. Future research will include testing the best evolved controllers by using them to fly real UAVs.
Development of an Internet Addressable Pneumatically Controlled Instrument for Applying Strain to Cells In-Vitro
(2006-06-14) Livingston, Frederick Jerard; Edward Grant, Committee Chair; Ola Harrysson, Committee Member; John Muth, Committee Member
Mechanical stimulation of tissue cells is a popular technique used by tissue engineering researchers to stimulate cell growth. This research requires an instrument that applies in-vitro compression and tension to individual cells, through mechanical loading. The mechanical load in the research reported on here is generated using a vacuum system under computer control. The vacuum system consists of a pneumatic valve that is proportionally controlled from a single board computer, and a pressure transducer to monitor the waveform of the applied mechanical loading. Because the computer control is based on a single-board computer, the mechanical loading of cells can be carried remotely using a network environment and a dedicated IP address. The system is a good example of a smart mechatronic system. The research and development was carried out with support from Flexcell International, a North Carolina based biotechnology company.
Electronic Textile-Based Sensors and Systems for Long-Term Health Monitoring
(2008-07-21) Merritt, Carey Reid; Edward Grant, Committee Chair; H. Troy Nagle, Committee Member; Behnam Pourdeyhimi, Committee Member; John Muth, Committee Member; John Wilson, Committee Member
Personalized long-term health monitoring has the potential to improve medicine's capabilities for diagnosing and correctly treating diseases at an early stage. Recently, progress has been made towards producing clothing that is suitable for such long-term monitoring. This work first reviews the current electronic textile based sensors that are used to measure two vital healthcare parameters, ECG and respiration. The techniques used for designing and fabricating these sensors are discussed and summarized. Furthermore, recommendations are proposed in regards to the development of an unobtrusive, wireless health monitoring garment. The second part of this research involved designing and fabricating two versions of fabric based active electrodes to provide a solution for long-term ECG monitoring clothing. The first version of active electrode involved attaching surface mountable components directly to a textile screen printed circuit using polymer thick film techniques. The second version involved attaching a significantly smaller active electrode interposer board to a simplified electronic textile circuit. Results from ECG tests on the active electrodes indicate that the performance of these new devices is comparable to commercial Ag⁄AgCl electrodes. The interposer based active electrodes were even found capable of surviving a five cycle washing test. This research also explores the potential for using capacitive sensing to serve as an inexpensive method for long-term respiration sensing. Two capacitive sensors were designed and fabricated for detecting chest or abdominal circumference changes. These sensors gave good linearity, sensitivity, and resolution. Respiration measurements obtained with these new sensors that were implemented into a prototype belt show that they are capable of measuring respiration rate and possibly lung function parameters. Finally this research presented a new modular wireless sensor node (MWSN) system for health monitoring clothing applications. The applications for this research involved integrating the MWSN into a custom designed ECG belt, a capacitive sensor respiration belt and an activity patch. Results obtained from these applications demonstrate that the MWSN is capable of interfacing with a diverse selection of health monitoring sensors while maintaining signal fidelity.
An Investigation and Expansion of Musculoskeletal Modeling and Analysis Techniques
(2008-08-05) Kelly, John Wade; Edward Grant, Committee Chair; Carol Giuliani, Committee Member; H. Troy Nagle, Committee Member
A Medical Robotic System for Laser Phonomicrosurgery
(2008-11-26) Giallo, Joseph Francis II; David Lalush, Committee Member; H. Troy Nagle, Committee Member; Charles Finley, Committee Member; Robert Buckmire, Committee Member; Edward Grant, Committee Chair
Phonomicrosurgery is a suite of complex otolaryngological surgical techniques related to the vocal folds. The primary operative challenges involve size and scaleability particularly when the carbon dioxide surgical laser is the operative tool of choice. The prevalent traditional methodology for remote control of the surgical laser is the mechanical micromanipulator. This device is capable of accurate laser aiming but is prone to error resulting from inexperience and ergonomic factors. Extensive training is required to employ the manual micromanipulator effectively. Many of the difficulties associated with use of the mechanical micromanipulator are rooted in the ergonomics of the device. By necessity it is located in a disadvantageous position, i.e., attached to the base of the surgical microscope. As a result, the clinician has no convenient way of steadying his/her hand while making the precise, delicate movements necessary to accurately and consistently aim the surgical laser. The fact that the required movements are relatively small in nature exacerbates the accuracy and consistency problem. The purpose of this dissertation is to document the design, development and application of a medical robotic system intended to improve this man machine interface. The primary goal of this research is the development of a device that moderates the operational challenges inherent in the classic manual micromanipulator, thereby enabling advances in clinical accuracy and efficiency.
Rapid Protoyping of a Single-Channel Electroencephalogram-Based Brain-Computer Interface
(2006-11-22) Adcock, David Brooks, Jr; John Muth, Committee Member; Lianne Cartee, Committee Co-Chair; Edward Grant, Committee Chair
This work describes the design, construction and implementation of a single-channel, electroencephalogram-based (EEG) brain-computer interface (BCI) for the prediction of a single-degree-of-freedom kinematic variable. The system employs a custom-built EEG amplifier to increase noise rejection and decrease the overall cost of the BCI. The EEG amplifier output is read into Matlab synchronously with an analog elbow-angle measurement taken from the test subject's left arm. Sampling is done at 300Hz using a 12-bit National Instruments PCI-6025E data acquisition card. Data is software filtered, processed, and logged in Matlab in real-time on a standard PC. At the end of an initial data acquisition period, a feed-forward backpropagation artificial neural network (ANN) is briefly trained off-line to predict subject elbow angle based solely on recorded EEG activity. Upon resuming recording, the system is accurately able to predict the test subject's elbow angle in real-time. If employed in a robotic system, this BCI would have applications in rehabilitation robotics, search and rescue, tele-robotics and exoskeleton research.
Towards the Automation of Embryonic Stem Cell Microinjections into Blastocysts
(2009-05-18) Mattos, Leonardo Serra; Edward Grant, Committee Chair; Donald Bitzer, Committee Member; Troy Nagle, Committee Member; John Muth, Committee Member; Randy Thresher, Committee Member
The purpose of the research has been to increase the consistency and efficiency rates of blastocyst microinjections through automation. The research involved the design, implementation, and evaluation of a novel biomanipulation system that is a test-bed for applying intelligent control algorithms. The microinjection process was controlled from a computer via a joystick or by software controllers. These included real-time video processing for the acquisition of experimental data and control. Teleoperated microinjections under the control of both expert and novice operators showed that the system is effective, easy to use, and capable of eliminating the need for the extensive training of microinjection personnel. Experimental results showed that all operators obtained a microinjection success rate over 80%, demonstrating a significant improvement over the tradition manual microinjections. Furthermore, blastocysts injected using this system were more likely to develop to term, and to yield chimeras, than blastocysts injected using the traditional manual method. The experiments also highlighted common problems encountered during the blastocyst microinjection stage, allowing the design and development of effective control algorithms to guide the teleoperated and automatic microinjections. Overall, this research contributed to the full automation of blastocyst microinjection by: 1) significantly improving the microinjection process; 2) significantly improving the microinjection efficiency; 3) creating a new system design optimized for computer controlled microinjections; 4) implementing and evaluating speed-up methods that enable real-time template matching; 5) creating new algorithms to identify and analyze blastocyst images; 6) designing and conducting preliminary tests with control algorithms that automate the microinjection process.