Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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2011 - 20182

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    A Self-Sealing Suction Technology for Versatile Grasping
    (2018) Kessens, Chad; Desai, Jaydev P; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This thesis describes the design, development, and evaluation of a novel "self-sealing" suction technology for grasping. As humans desire robots capable of handling an increasingly diverse set of tasks, end effectors that are able to grasp the widest possible range of object shapes and sizes will be needed to achieve the desired versatility. Technologies enabling the exertion of local pulling contact forces (e.g. suction) can be extraordinarily useful toward this end by handling objects that do not have features smaller than the grasper, a challenge for traditional grippers. However, simple operation and cost effectiveness are also highly desirable. To achieve these goals, we have developed a self-sealing suction technology for grasping. A small valve inside each suction cup nominally seals the suction port to maintain a vacuum within the system. Through the reaction forces of object contact, a lever action passively lifts the valve to engage suction on the object. Any cups not contacting the object remain sealed. In this way, a system with a large number of cups may effectively operate using any subset of its cups, even just one, to grasp an object. All cups may be connected to a central vacuum source without the need for local sensors or powered actuators for operation, forming a simple, compact, cost effective system. This thesis begins with the detailed design and analysis of the self-sealing suction technology. An extensive evaluation of the technology's robustness and performance demonstrates its features and limits. This includes self-seal quality and leakage, object seal and reseal, cycle performance, and normal and shear force-displacement, among other characterizations. It then describes the development of several devices utilizing the technology. The potential impact of the technology is highlighted through applications of human-controlled, robotic, and aerial grasping and perching. Finally, mathematical tools are developed to analyze potential grasps developed using the technology.
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    Decoding Repetitive Finger Movements with Brain Signals Acquired Via Noninvasive Electroencephalography
    (2011) Paek, Andrew Young; Contreras-Vidal, Jose L; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    We investigated how well finger movements can be decoded from electroencephalography (EEG) signals. 18 hand joint angles were measured simultaneously with 64-channel EEG while subjects performed a repetitive finger tapping task. A linear decoder with memory was used to predict continuous index finger angular velocities from EEG signals. A genetic algorithm was used to select EEG channels across temporal lags between the EEG and kinematics recordings, which optimized decoding accuracies. To evaluate the accuracy of the decoder, the Pearson's correlation coefficient (r) between the observed and predicted trajectories was calculated in a 10-fold cross-validation scheme. Our results (median r = .403, maximum r = .704), compare favorably with previous studies that used electrocorticography (ECoG) to decode finger movements. The decoder used in this study can be used for future brain machine interfaces, where individuals can control peripheral devices through EEG signals.