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.

A NARRATIVE CASE STUDY EXAMINING THE EXPEREINCES OF MEN TEACHING SPECIAL EDUCATION IN THE ELEMENTARY GRADES

Over the past several decades, the landscape of the workplace has changed in many industrialized nations. In the United States this time period has seen the outright elimination or outsourcing of well-paying “blue collar” jobs. The workforce continues to evolve, change, and become more global, and men and women are making nontraditional occupational decisions, whether by choice or necessity. The traditional views of men and women have begun to shift. However, gender assumptions about masculinity have failed to keep pace with the shift. There are approximately 1.8 million elementary grade level teachers in United States public schools; of these, a mere 9% are male. The paucity of male teachers in the elementary grades has been a concern for many years. According to the Bureau of Labor Statistics, roughly 86% of all special education teachers are female. In 2012, 86.2% of all special education teachers were female, and by the following year, the number had dropped to 80.4%. The evidence indicates that more men are embarking on nontraditional career paths. Despite theses changes there is minimal research looking at the experiences of men working as special education teachers My goal in this study was to obtain a better understanding of the influences on and the process by which men make the decision to pursuing a career teaching special education in the elementary grades. The study utilized social role theory (Eagly, 1987), and Stead’s (2014) social constructionist theory as well as Williams’ (1992) glass escalator proposition The findings of this study confirm some of the factors related to career choice, experiences and barriers faced by men in nontraditional careers detailed in the literature. Three themes emerged for each research question: Experiences, advocacy, and benefits. Three themes emerged around the second research question exploring the experiences of men in a female-concentrated profession: The male body, communication, and perception. Three themes arose around the third research question: administration, My Masculinity, and pay. The findings run counter to Williams’ glass escalator proposition, which posits men working in female-concentrated professions are at an advantage. The findings advance support for Buschmeyer’s theory of (2013) alternative masculinity.