CONCURRENT LOCALIZATION AND MAPPING WITH SONAR SENSORS AND CONSIDERATION OF VEHICLE MOTION

Simultaneous Localization and Mapping (SLAM) is a procedure used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. A typical SLAM consists of four main components, namely, experimental setup (data gathering), vehicle pose estimation, feature extraction, and filtering. Feature extraction is the process of realizing significant features from the unknown environment such as corners, edges, walls, and interior features. In this work, an original feature extraction algorithm specific to distance measurements obtained through SONAR sensor data is presented. This algorithm has been constructed by combining the SONAR Salient Feature Extraction Algorithm and the Triangulation Hough Based Fusion with point-in-polygon detection. The reconstructed maps obtained through simulations and experimental data with the fusion algorithm are compared to the maps obtained with existing feature extraction algorithms. Based on the results obtained, it is suggested that the proposed algorithm can be employed as an option for data obtained from SONAR sensors in environment, where other forms of sensing are not viable. The algorithm fusion for feature extraction requires the vehicle pose estimation as an input, which is obtained from a vehicle pose estimation model. For the vehicle pose estimation, the author uses sensor integration to estimate the pose of the mobile vehicle. Different combinations of these sensors are studied (e.g., encoder, gyroscope, or encoder and gyroscope). The different sensor fusion techniques for the pose estimation are experimentally studied and compared. The vehicle pose estimation model, which produces the least amount of error, is used to generate inputs for the feature extraction algorithm fusion. In the experimental studies, two different environmental configurations are used, one without interior features and another one with two interior features. Numerical and experimental findings are discussed. Finally, the SLAM algorithm is implemented along with the algorithms for feature extraction and vehicle pose estimation. Three different cases are experimentally studied, with the floor of the environment intentionally altered to induce slipping. Results obtained for implementations with and without SLAM are compared and discussed. The present work represents a step towards the realization of autonomous inspection platforms for performing concurrent localization and mapping in harsh environments.

Regular homomorphisms of minimal sets

The classification of minimal sets is a central theme in abstract topological dynamics. Recently this work has been strengthened and extended by consideration of homomorphisms. Background material is presented in Chapter I. Given a flow on a compact Hausdorff space, the action extends naturally to the space of closed subsets, taken with the Hausdorff topology. These hyperspaces are discussed and used to give a new characterization of almost periodic homomorphisms. Regular minimal sets may be described as minimal subsets of enveloping semigroups. Regular homomorphisms are defined in Chapter II by extending this notion to homomorphisms with minimal range. Several characterizations are obtained. In Chapter III, some additional results on homomorphisms are obtained by relativizing enveloping semigroup notions. In Veech's paper on point distal flows, hyperspaces are used to associate an almost one-to-one homomorphism with a given homomorphism of metric minimal sets. In Chapter IV, a non-metric generalization of this construction is studied in detail using the new notion of a highly proximal homomorphism. An abstract characterization is obtained, involving only the abstract properties of homomorphisms. A strengthened version of the Veech Structure Theorem for point distal flows is proved. In Chapter V, the work in the earlier chapters is applied to the study of homomorphisms for which the almost periodic elements of the associated hyperspace are all finite. In the metric case, this is equivalent to having at least one fiber finite. Strong results are obtained by first assuming regularity, and then assuming that the relative proximal relation is closed as well.

Design of Nanophotonic Antireflection Coatings for III-V Thin-Film Photovoltaics

Thin-film photovoltaics have provided a critical design avenue to help decrease the overall cost of solar power. However, a major drawback of thin-film solar cell technology is decreased optical absorption, making compact, high-quality antireflection coatings of critical importance to ensure that all available light enters the cell. In this thesis, we describe high efficiency thin-film InP and GaAs solar cells that utilize a periodic array of nanocylinders as antireflection coatings. We use coupled optical and electrical simulations to find that these nanophotonic structures reduce the solar-weighted average reflectivity of InP and GaAs solar cells to around 1.3 %, outperforming the best double-layer antireflection coatings. The coupling between Mie scattering resonances and thin-film interference effects accurately describes the optical enhancement provided by the nanocylinders. The spectrally resolved reflectivity and J-V characteristics of the devices under AM1.5G solar illumination are determined via the coupled optical and electrical simulations, resulting in predicted power conversion efficiencies > 23 %. We conclude that the nanostructured coatings reduce reflection without negatively affecting the electronic properties of the InP and GaAs solar cells by separating the nanostructured optical components from the active layer of the device.