<p> Traditionally, models of a robot's kinematics and sensors have been provided by designers through manual processes. Such models are used for sensorimotor tasks, such as manipulation and stereo vision. However, these techniques often yield static models based on one-time calibrations or ideal engineering drawings; models that often fail to represent the actual hardware, or in which individual unimodal models, such as those describing kinematics and vision, may disagree with each other.</p><p> Humans, on the other hand, are not so limited. One of the earliest forms of self-knowledge learned during infancy is knowledge of the body and senses. Infants learn about their bodies and senses through the experience of using them in conjunction with each other. Inspired by this early form of self-awareness, the research presented in this thesis attempts to enable robots to learn unified models of themselves through data sampled during operation. In the presented experiments, an upper torso humanoid robot, Nico, creates a highly-accurate self-representation through data sampled by its sensors while it operates. The power of this model is demonstrated through a novel robot vision task in which the robot infers the visual perspective representing reflections in a mirror by watching its own motion reflected therein.</p><p> In order to construct this self-model, the robot first infers the kinematic parameters describing its arm. This is first demonstrated using an external motion capture system, then implemented in the robot's stereo vision system. In a process inspired by infant development, the robot then mutually refines its kinematic and stereo vision calibrations, using its kinematic structure as the invariant against which the system is calibrated. The product of this procedure is a very precise mutual calibration between these two, traditionally separate, models, producing a single, unified self-model.</p><p> The robot then uses this self-model to perform a unique vision task. Knowledge of its body and senses enable the robot to infer the position of a mirror placed in its environment. From this, an estimate of the visual perspective describing reflections in the mirror is computed, which is subsequently refined over the expected position of images of the robot's end-effector as reflected in the mirror, and their real-world, imaged counterparts. The computed visual perspective enables the robot to use the mirror as an instrument for spacial reasoning, by viewing the world from its perspective. This test utilizes knowledge that the robot has inferred about itself through experience, and approximates tests of mirror use that are used as a benchmark of self-awareness in human infants and animals.</p>

Thèse (M.Sc.)--Université de Sherbrooke (Canada), 2007. / Titre de l'écran-titre (visionné le 1 février 2007). In ProQuest dissertations and theses. Publié aussi en version papier.

Thèse (M.Sc.A.)--Université de Sherbrooke (Canada), 2007. / Titre de l'écran-titre (visionné le 1 février 2007). In ProQuest dissertations and theses. Publié aussi en version papier.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. / Source: Dissertation Abstracts International, Volume: 69-11, Section: B, page: 6922. Adviser: Jean Ponce. Includes bibliographical references (leaves 127-134) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. / Source: Dissertation Abstracts International, Volume: 69-11, Section: B, page: 7038. Adviser: Mark W. Spong. Includes bibliographical references (leaves 140-157) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6625. Adviser: Stephen Levinson. Includes bibliographical references (leaves 71-77) Available on microfilm from Pro Quest Information and Learning.

<p> Detecting leaks is always a priority in the oil and gas industry and plays a major concern to human safety. The time required to fix any leak has a direct relationship in determining the damages caused to the environment, industry, and most importantly, the number of lives lost caused by catastrophic pipe failures. Detecting leak size and location in pipelines with higher accuracy presents major challenges to operators. This research work presents an innovative solution to locate a leak location inside a pipeline with higher precision. The solution is based on generating a 3D model that establishes a relationship between leak noise and its associated location and size. In order to generate the 3D model, an experiment study was first conducted where a flow loop having a leak, integrated with an acoustic detection system, was built to collect data about the effect of leak size, flowrate, pipeline material, and length on the noise generated. Later, a numerical study used the experimental results to initiate a simulation that aimed at finding how the leak noise propagates from the leak location. Finally, the experimental and numerical results were combined into a 3D model equation that solves for the leak location based on the leak noise and size.</p><p>

Despite the prevalence of insect flight as a form of locomotion in nature, manmade aerial systems have yet to match the aerial prowess of flying insects. Within a tiny body volume, flying insects embody the capabilities to flap seemingly insubstantial wings at very high frequencies and sustain beyond their own body weight in flight. A precise authority over their wing motions enables them to respond to obstacles and threats in flight with unrivaled speed and grace. Motivated by a desire for comparably agile flying machines, research efforts in the last decade have generated crucial developments for realizing an artificial instantiation of insect flight. The need for tiny, high-efficiency mechanical components has produced unconventional solutions for propulsion, actuation, and manufacturing. Early vehicle designs proved to be flightworthy but were critically limited by the inability to produce control torques in flight. In this thesis, we synthesize all existing technologies for insect-scale manufacturing and actuation, and we introduce a new vehicle design, the "dual actuator bee," to address the need for flight control. Our work culminates in the first demonstration of controlled, hovering flight of an insect-scale, flapping-wing robot. As the ultimate goal for this research effort is the creation of fully autonomous flying robots, these vehicles must sustain their own power sources and intelligence. To that end, we explore the challenges of scaling flapping-wing flight to attain greater lift forces. Using a scaling heuristic to determine key vehicle specifications, we develop and successfully demonstrate a hover-capable vehicle design that possesses the requisite payload capacity for the full suite of components required for control autonomy. With this operational vehicle as a point of reference, we introduce an iterative sizing procedure for specifying a vehicle design with payload capacity capable of supporting power autonomy. In the development of these vehicles, the reliability of their construction has been a substantial challenge. We present strategies for systematically addressing issues of vehicle construction. Together, this suite of results demonstrates the feasibility of achieving artificial, insect-like flight. / Engineering and Applied Sciences - Engineering Sciences

In the past three decades, textile fiber production has grown more than twofold, reflecting an increasing demand for both fossil and natural resources (Turley et. al., 2009, p.9) resulting in the intensification of environmental impacts such as water scarcity, abiotic depletion, and toxic pollution. Although potentially disruptive technology such as three-dimensional printing (3DP) can eliminate entire supply chain components and potentially reduce energy and water in textile and apparel production, 3DP as an application for clothing production is still at its nascent stage of development––the world’s first 3D-printed garment being created only in 2010. As prevention is preferable to mitigation, it is timely to examine key environmental impacts of potentially disruptive technology in advance of its proliferation. By using environmental management tools such as ISO14044:2006 and the life cycle inventory analysis (LCI) method to identify hotspots for two key impact areas––water and energy consumption––of prevalent 3D printing methods versus that of the conventional cut-and-sew method in clothing production, environmental sustainability parameters can then be established to target underperforming areas and improve the design, manufacture, use, and disposal of future garments. Using one standard T-shirt as a functional unit, this meta-life cycle inventory analysis will compare the energy and water demands of using three main AM techniques––selective laser sintering (SLS), fused deposition modeling (FDM), and field guided fabrication (FGF)––against that of using the conventional cut-and-sew method to create one standard T-shirt out of six most representative materials.

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