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Description
Human fingertips contain thousands of specialized mechanoreceptors that enable effortless physical interactions with the environment. Haptic perception capabilities enable grasp and manipulation in the absence of visual feedback, as when reaching into one's pocket or wrapping a belt around oneself. Unfortunately, state-of-the-art artificial tactile sensors and processing algorithms are no match for their biological counterparts. Tactile sensors must not only meet stringent practical specifications for everyday use, but their signals must be processed and interpreted within hundreds of milliseconds. Control of artificial manipulators, ranging from prosthetic hands to bomb defusal robots, requires a constant reliance on visual feedback that is not entirely practical. To address this, we conducted three studies aimed at advancing artificial haptic intelligence. First, we developed a novel, robust, microfluidic tactile sensor skin capable of measuring normal forces on flat or curved surfaces, such as a fingertip. The sensor consists of microchannels in an elastomer filled with a liquid metal alloy. The fluid serves as both electrical interconnects and tunable capacitive sensing units, and enables functionality despite substantial deformation. The second study investigated the use of a commercially-available, multimodal tactile sensor (BioTac sensor, SynTouch) to characterize edge orientation with respect to a body fixed reference frame, such as a fingertip. Trained on data from a robot testbed, a support vector regression model was developed to relate haptic exploration actions to perception of edge orientation. The model performed comparably to humans for estimating edge orientation. Finally, the robot testbed was used to perceive small, finger-sized geometric features. The efficiency and accuracy of different haptic exploratory procedures and supervised learning models were assessed for estimating feature properties such as type (bump, pit), order of curvature (flat, conical, spherical), and size. This study highlights the importance of tactile sensing in situations where other modalities fail, such as when the finger itself blocks line of sight. Insights from this work could be used to advance tactile sensor technology and haptic intelligence for artificial manipulators that improve quality of life, such as prosthetic hands and wheelchair-mounted robotic hands.
ContributorsPonce Wong, Ruben Dario (Author) / Santos, Veronica J (Thesis advisor) / Artemiadis, Panagiotis K (Committee member) / Helms Tillery, Stephen I (Committee member) / Posner, Jonathan D (Committee member) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Modern automotive and aerospace products are large cyber-physical system involving both software and hardware, composed of mechanical, electrical and electronic components. The increasing complexity of such systems is a major concern as it impacts development time and effort, as well as, initial and operational costs. Towards the goal of measuring complexity, the first step is to determine factors that contribute to it and metrics to qualify it. These complexity components can be further use to (a) estimate the cost of cyber-physical system, (b) develop methods that can reduce the cost of cyber-physical system and (c) make decision such as selecting one design from a set of possible solutions or variants. To determine the contributions to complexity we conducted survey at an aerospace company. We found out three types of contribution to the complexity of the system: Artifact complexity, Design process complexity and Manufacturing complexity. In all three domains, we found three types of metrics: size complexity, numeric complexity (degree of coupling) and technological complexity (solvability).We propose a formal representation for all three domains as graphs, but with different interpretations of entity (node) and relation (link) corresponding to the above three aspects. Complexities of these components are measured using algorithms defined in graph theory. Two experiments were conducted to check the meaningfulness and feasibility of the complexity metrics. First experiment was mechanical transmission and the scope of this experiment was component level. All the design stages, from concept to manufacturing, were considered in this experiment. The second experiment was conducted on hybrid powertrains. The scope of this experiment was assembly level and only artifact complexity is considered because of the limited resources. Finally the calibration of these complexity measures was conducted at an aerospace company but the results cannot be included in this thesis.
ContributorsGurpreet Singh (Author) / Shah, Jami (Thesis advisor) / Runger, George C. (Committee member) / Davidson, Joseph (Committee member) / Arizona State University (Publisher)
Created2011