Studying abroad during the collegiate experience is an idea sought by the many high school seniors and college freshmen alike. The social, intellectual, and cultural benefits received from participating makes it a seemingly easy decision. Yet, lack of action plagues the nation as there exists a miniscule number of students who follow through with their desire to study abroad. Prior research exemplifies that students rely on the perceptions of their peers and family when approaching an unfamiliar subject. These perceptions result in concocted barriers that dissuade the student from participating in study abroad before they seek concrete information. There are a limited number of studies completed at universities to identify these specific barriers. Thus, this research examines the student population at the University of Central Florida, the perceived barriers they possess concerning study abroad, and their sources of these perceptions. This is a qualitative and quantitative study that investigates the effects of perceived cost and graduation delay on the willingness of a student to study abroad. The unexpected ramifications of the marketing mix, utilized by the department of Study Abroad, will shed light upon the steps necessary to revise their position in order to transform student intent into action.

Virtual visualization of mobile robot analytical trajectories while avoiding moving obstacles is presented in this thesis as a very helpful technique to properly display and communicate simulation results. Analytical solutions to the path planning problem of mobile robots in the presence of obstacles and a dynamically changing environment have been presented in the current robotics and controls literature. These techniques have been demonstrated using two-dimensional graphical representation of simulation results. In this thesis, the analytical solution published by Dr. Zhihua Qu in December 2004 is used and simulated using a virtual visualization tool called VRML.

This thesis contains the design, manufacturing, and testing of a functional eight-legged robotic transporter based on the concept design laid out in U.S. Patent 7,246,671. The device is intended to achieve three different sequences of motion: regular driving, obstacle climbing, and stair climbing. The prototype was carried through concept design, analysis, selection of materials and components, manufacturing, software development, and final assembly and testing. The device can be assembled under multiple configurations, which harbor certain advantages and disadvantages. The results of the testing encourage the continuation of a second iteration of this concept.

Parkinson's disease (PD) is a neurological condition that can lead to changes to gait and postural stability of people with this condition. Tai Chi (TC) has been recommended for the management of PD by improving muscle strength, balance, and coordination. However, biomechanics research in TC for PD is lacking. This thesis investigated the effects of a biomechanical-based TC intervention program for people in the early-stage of PD by realizing three specific research objectives: 1) to develop a biomechanical-based TC intervention program for PD; 2) to examine the effects of a 12-week TC intervention on gait and postural stability in people with PD, by pre-test and post-test biomechanical analysis of obstacle crossing; 3) to explore the neuromuscular effects of TC intervention on gait and postural stability by analyzing the electromyography (EMG) activity of the lower limb muscles during obstacle crossing. Seven typical TC movements were selected, including Starting Form, Hero Touch Sky, Push Hand Back, Brush Knee and Twist Step, Repulse Monkey, Wave-hand in Cloud, and Lateral Forward Step to develop the TC intervention program. The joint angles, joint moment, and EMG signals of lower limbs muscles were analyzed during performance of the selected TC movements by an experienced TC master aged 38 years. Results showed that the selected TC movements are characterized by multidirectional movements, greater joint movement angles of the lower limb, and more active muscle activity than walking. The TC intervention program was formed based on the biomechanics analysis of the seven TC movements. The program consisted of 5-10 min warm up, 40 minutes of core activities, and 5 min cool down. To examine the effects of a biomechanical-based TC intervention program on gait and postural stability and to explore the neuromuscular effects of TC intervention on the lower limb muscles in people with PD, fifteen individuals in the early stage of PD (n = 15, Hoehn and Yahr stages 1 to 2; age 72.0 ± 6.9) participated in a 12-week online TC intervention, and 15 age- and sex-matched healthy participants (n = 15) served as control. The 3D motion data of the lower limb and EMG signals from the rectus femoris, adductor longus, tibialis anterior, semitendinosus, gluteus medius, tensor fasciae latae, and medial and lateral gastrocnemius muscles were collected during obstacle crossing from both groups using Vicon motion analysis system before intervention in both groups and after TC intervention in the TC group. Obstacle crossing was used to challenge the participants' gait and postural stability. Gait was assessed by measuring the temporospatial parameters such as crossing stride length, crossing step length, and crossing speed. Postural stability was assessed by measuring toe and clearance distance, pre- and post-horizontal distance, displacement and velocity of center of mass (COM), and COM-center of pressure (COP) separation. To examine the neuromuscular activity of the lower limbs following TC training, the ratio of the peak EMG, the integrated EMG (iEMG), and the ratio of the peak EMG and iEMG antagonistic pairs of the leading and trailing limb were examined during obstacle crossing and walking. In addition, the timed up-and-go test (TUG) and single-leg stance with eyes open and closed were tested. VICON Nexus, custom MATLAB scripts, and SPSS software (version 20) were used to analyze the data. Analysis of the PD participants' obstacle crossing performance revealed that they had significantly slower gait speed, smaller hip flexion angles, and larger knee adduction angles of the trailing limb, significantly larger mediolateral (ML) COM displacement and COM-COP separation distance, and significantly higher peak EMG ratios of the adductor longus, gluteus medius, and tensor fasciae latae (p < 0.05) than the healthy participants. Following the 12-week TC intervention, the PD participants had significantly increased crossing stride length, significantly decreased ML COM-COP separation (p < 0.05) and significantly increased AP COM displacement (p < 0.05), and increased EMG activity of the tibialis anterior in the leading and trailing limbs during obstacle crossing, whereas the activity of the gluteus medius and adductor longus in the leading limb decreased (p < 0.05). Moreover, the significant differences in the EMG of the gluteus medius and tensor fasciae latae muscles between PD and healthy participants found in pre-test were no longer present. After the TC intervention, the PD participants significantly improved their performance on the TUG test (p = 0.002). Therefore, people with early-stage PD presented changed gait and postural stability as well as changed neuromuscular activity of the lower limb. The 12-week online biomechanical-based TC intervention improved their gait and postural stability, particularly dynamic postural stability as measured by the COM-COP separation in people with early-stage PD. Compared to walking, performing the TC movements involved larger changes in the lower limb angles, range of motion, and higher muscle activity, particularly in the hip abductors and adductors. These characteristics of the TC movements could provide training to strengthen the muscles and improve the range of motion of the lower limbs. The gait and postural stability improvements following TC intervention are consistent with the neuromuscular activity changes in gluteus medius, tensor fasciae latae, and adductor longus muscles, indicating the mechanisms of TC training. The 12-week online biomechanical-based TC intervention program helped to decrease the ML COM-COP separation distance and could be used for the management of PD in the early stages of this condition.

Many people with multiple sclerosis (MS) have difficulty with walking, which can decrease their sense of mobility. Gait stability was investigated by studying stride parameters and head movements at preferred and fixed speeds in those with MS. First, walking gait data were recorded at preferred and fixed walking speeds from 19 individuals with MS and 19 controls. Traditional gait parameters were compared, as was swing foot to center of mass (CoM) timing at mid-swing. Second, walking gait data in healthy young adults (n=20) were recorded at preferred speed and while stepping over an obstacle. Study 2 developed novel swing definitions, measures of coordination between the swing foot and body CoM, and head movements as they pertain to field of view orientation during walking. Third, these novel measures were used to study the swing phase of walking in people with MS. The first investigation revealed that the MS group walked with lengthened dual support times across all speeds, but shortened swing time and altered swing foot timing at fixed speeds in comparison to controls. Those with MS adopted a gait strategy with increased dual support time, despite forcing changes to swing that may reduce gait stability. In the second investigation, novel measures of swing showed alterations to phases of swing and in coordination between the swing foot and CoM under different gait tasks. This study also showed that the field of view was closer to the body during obstacle condition steps compared with unobstructed gait. In the third study, these novel measures showed that at all speeds the MS group shortened early swing and lengthened mid swing while late swing remained unchanged compared with controls. Coordination measures illustrated adaptations in swing foot dynamics that may partially ameliorate altered swing foot timing. The MS group oriented the field of view closer to the body earlier in swing compared with controls. Those with MS have functionally adapted swing to increase time over the stance foot and rely more on visual perception, yet shorter early swing may afford fewer opportunities to plan a step or cope with gait disturbances while walking.

Scene perception and traversability analysis are real challenges for autonomous driving systems. In the context of off-road autonomy, there are additional challenges due to the unstructured environments and the existence of various vegetation types. It is necessary for the Autonomous Ground Vehicles (AGVs) to be able to identify obstacles and load-bearing surfaces in the terrain to ensure a safe navigation (McDaniel et al. 2012). The presence of vegetation in off-road autonomy applications presents unique challenges for scene understanding: 1) understory vegetation makes it difficult to detect obstacles or to identify load-bearing surfaces; and 2) trees are usually regarded as obstacles even though only trunks of the trees pose collision risk in navigation. The overarching goal of this dissertation was to study traversability analysis in unstructured forested terrains for off-road autonomy using LIDAR data. More specifically, to address the aforementioned challenges, this dissertation studied the impacts of the understory vegetation density on the solid obstacle detection performance of the off-road autonomous systems. By leveraging a physics-based autonomous driving simulator, a classification-based machine learning framework was proposed for obstacle detection based on point cloud data captured by LIDAR. Features were extracted based on a cumulative approach meaning that information related to each feature was updated at each timeframe when new data was collected by LIDAR. It was concluded that the increase in the density of understory vegetation adversely affected the classification performance in correctly detecting solid obstacles. Additionally, a regression-based framework was proposed for estimating the understory vegetation density for safe path planning purposes according to which the traversabilty risk level was regarded as a function of estimated density. Thus, the denser the predicted density of an area, the higher the risk of collision if the AGV traversed through that area. Finally, for the trees in the terrain, the dissertation investigated statistical features that can be used in machine learning algorithms to differentiate trees from solid obstacles in the context of forested off-road scenes. Using the proposed extracted features, the classification algorithm was able to generate high precision results for differentiating trees from solid obstacles. Such differentiation can result in more optimized path planning in off-road applications.

This compilation thesis presents two main contributions in path planning and obstacle avoidance, as well as an integration of the proposed modules with other frameworks to enable resilient robotic missions in complex environments.In general, through different types of robotic missions it is important to have a collision tolerant and reliable system, both regarding potential risks from collisions with dynamic and static obstacles, but also to secure the overall mission success.%Particularly, a common trend in the presented work is safety regarding collisions with dynamic and static obstacles, as well as reliable overall systems that are capable of executing missions. The work included in this thesis presents the risk-aware path planner D$^*_+$ that is capable of planning traversable paths for both ground and aerial robots. D$^*_+$ is developed on top of D$^*$-lite with a risk layer close to occupied space, modeling the unknown areas as a risk, and is implemented with a dynamic map to enable updates and adjustments to a changing environment. The risk layer aids in solving two common challenges with path planning for real robots: a) it creates a safety margin that gives free space between the path and obstacles so that robots with the corresponding size can follow the path, and b) it masks smaller holes in walls that occur when building maps from real data. Using a dynamic map makes it possible to use D$^*_+$ for an exploration mission, it also enables for the re-planning of the path if the environment changes for example, if an obstacle suddenly blocks a path, a new path will be planned. D$^*_+$ have been tested in different real-life experiments with both an Unmanned Areal Vehicle (UAV) and a quadruped-legged robot and shown to produce traversable paths in different application scenarios, such as exploration, return to base, and navigation on known maps. This thesis also presents an obstacle avoidance architecture for velocity objects, structured around an object detection and tracking scheme that is combined with non-linear model predictive controller (NMPC) to plan the avoidance maneuver. %that uses a Convolutional Neural Network to detect obstacles that are tracked so they can be avoided by a non-linear model predictive controller (NMPC).In this case, the detection is done with the Convoluitonal Neural Network (CNN) You Only Lock Once v4 (YOLO) where the most certain human is tracked with a Kalman filter, and the velocity of the human is estimated.The proposed scheme models the object motion as constant velocity, which is utilized from the NMPC to plan control inputs for the robot to avoid the identified obstacle. A merit of the approach is that the avoidance maneuver does not only consider the current identification position, but also considers the motion prediction of the object. This avoidance framework proved to be capable to avoid non-cooperative obstacles, such as humans moving towards it.Due to the fact that the avoidance is starting when a future collision is predicted, the avoidance maneuver is started early enough to avoid obstacles with a higher velocity than a classic ``static obstacle'' radius approach can handle. An additional aim of this thesis is to showcase that the proposed contributions can be applied in full robotic missions/frameworks. Thus, this thesis presents a search and rescue mission with a quadruped-legged robot and a UAV on a partially known map to find the location of survivors and other objects of related interest. In this mission, the quadruped-legged robot carries the UAV to the edge of the known map from where it launches the UAV that then explores and detects any survival and other relevant objects.Also, an autonomy solution, based on Boston dynamics' quadruped-legged robot Spot, for enabling a map-based navigation in confined environments has been developed and tested. This Spot solution enables the robot to navigate to a user-selected point, rotate in the desired direction, and instruct the UAV, in the combined search and rescue mission, to take off.

This study addresses the application and the effectiveness of radar obstacle sensors for forklift trucks during reverse travel. Two different discriminating radar obstacle sensors with different outputs are evaluated. This study reviews the safety of human exposure to emissions from these radar sensors; documents the field of view obtained from experiments with the two systems; gives the results from experiments with sensors on lift trucks. The influence of obstacle reflectivity, composition and area on the size and shape of the radar detection zone are discussed. An experimental setup for measuring position and velocity of the obstacle crossing the truck path is described. The combination of obstacle sensors required for full coverage of the back of the lift trucks and the mounting height and angle are discussed.

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