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Internet och dess webbplatser är källa till mycket information. Ofta vet vi vilken information vi söker, problemet är bara att hitta den. Ett nyckelelement på webbplatser för att hitta den information som man söker är navigationen. Navigationen kan vara utformad på många olika sätt, vilka har olika för- och nackdelar. Denna studie ska undersöka vilken navigationstyp som är den mest effektiva för tredimensionella navigationsstrukturer.Först väljs de fem navigationstyperna som ska jämföras, detta genom att undersöka vilka de bästa webbplatserna i Sverige använder. För att kunna testa de mot varandra byggs en testmiljö i fem versioner. Det enda som skiljer versionerna åt är deras navigationstyp. Fem testpersoner för varje testmiljö får sedan sex uppgifter att utföra. Här mäts tid för att utföra uppgiften, tid som spenderas i navigationen för varje uppgift, antal klick/hovringar samt antal felklick/felhovringar. Det samlas även in demografisk data samt testpersonens subjektiva uppfattning om testmiljöns navigationstyp.Resultatet av undersökningen visar att navigationstypen mega drop-down är effektivast enligt de uppmätta mätvariablerna. Den har genomgående bäst testresultat, både tidsmässigt och klick/hovringsmässigt. Mellan de övriga fyra navigationstyperna visar undersökningen ingen större skillnad. Studien rekommenderar därför att använda mega drop-down vid implementation av en tredimensionell navigationsstruktur. / Internet with all its websites is a great source of information. Often we know what information we are searching for and the problem is to find it. A key element to find information on websites is the navigation, which can be designed in many different ways with different advantages and disadvantages. This study will examine which type of navigation that is the most effective for three-dimensional navigation structures.At first five different navigation types are chosen to be examined by reviewing what sort of navigation type are used in the top 50 web pages in Sweden. In order to test the different types against each other a test environment is built in five different versions. The only thing separating them is the navigation type that is implemented. Five test subjects are then handed six tasks to complete in one test environment each. The variables that are measured are the total time for completing the task, the time spent in the navigation, the total number of clicks/hovers and the number of errors caused by each click/hover. Demographic data and subjective opinions about the navigation systems are also collected from the test subjects.The result of the survey shows that the mega drop-down navigation is the most effective navigation type according to the measured variables. It consistently keeps the highest result in all measured variables included time tracking, amount of clicks/hovers and the subjective opinion by the test objects. Between the other four navigation-types the test shows no major difference. The study therefore recommends the use of mega drop-down navigation in implementation of three-dimensional navigational structures.

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Display technology has developed significantly over the last decade, and it is becoming increasingly feasible to construct large, high-resolution displays. Prior work has shown a number of key performance advantages for these displays that can largely be attributed to the replacement of virtual navigation (e.g., panning and zooming) with physical navigation (e.g., moving, turning, glancing). This research moves beyond the question of performance or efficiency and examines ways in which the large, high-resolution display can support the cognitive demanding task of sensemaking. The core contribution of this work is to show that the physical properties of large, high- resolution displays create a fundamentally different environment from conventional displays, one that is inherently spatial, resulting in support for a greater range of embodied resources. To support this, we describe a series of studies that examined the process of sensemaking on one of these displays. These studies illustrate how the display becomes a cognitive partner of the the analyst, encouraging the use of the space for the externalization of the analyst's thought process or findings. We particularly highlight how the flexibility of the space sup- ports the use of incremental formalism, a process of gradually structuring information as understanding grows. Building on these observations, we have developed a new sensemaking environment called Analyst's Workspace (AW), which makes use of a large, high-resolution display as a core component of its design. The primary goal of AW is to provide an environment that unifies the activities of foraging and synthesis into a single investigative thread. AW addresses this goal through the use of an integrated spatial environment in which full text documents serve as primary sources of information, investigative tools for pursuing leads, and sensemaking artifacts that can be arranged in the space to encode information about relationships between events and entities. This work also provides a collection of design principles that fell out of the development of AW, and that we hope can guide future development of analytic tools on large, high-resolution displays. / Ph. D.

The purpose of this study was to examine the effect of various navigational control options within a hypermedia learning environment on the performance of students who differed in their locus of control orientations. Ninety-three college students were classified as internal or external in their locus of control orientation based on their scores on the Adult Nowicki-Strickland Locus of Control Scale (ANSIE). They were then randomly assigned to one of three treatment groups that differed in the way participants navigated through a hypermedia instructional program dealing with the human heart. In the Linear group, participants navigated through the program in a standard linear fashion. In the Branching group, participants navigated through the program with the help of a hierarchical menu structure. In the Networked group, participants had the additional option of using embedded (associative) hyperlinks. At the conclusion of the program, participants completed a posttest that assessed two types of learning. A 2 X 3 Analysis of Variance was conducted to explore the main effects for locus of control (internal and external) and navigational control (linear, branching, and networked) and any interaction effect between the two factors. The results showed no significant differences in achievement based on participants' locus of control orientation or treatment group. There was also no significant interaction observed. The results provided no support for the hypothesis that different navigation options would improve the performance of learners differing in their locus of control orientation. / Ph. D.

If “a picture is worth a thousand words,” then a video may be worth a thousand pictures. Videos have been increasingly used in multiple applications, including surveillance, teleconferencing, learning and experience sharing. Since a video captures a scene from a particular viewpoint, it can often be understood better if presented within a larger spatial context. We call such interactive visualizations that combine videos with their spatial context "Contextualized Videos". Over recent years, multiple innovative Contextualized Video interfaces have been proposed to taking advantage of the latest computer graphics and video processing technologies. These interfaces opened a huge design space with numerous design possibilities, each with its own benefits and limitations. To avoid piecemeal understanding of the design space, this dissertation systematically designs and evaluates Contextualized Video interfaces based on a taxonomy of tasks that can potentially benefit from Contextualized Videos. This dissertation first formalizes a design space. New designs are created incrementally along the four major dimensions of the design space. These designs are then empirically compared through a series of controlled experiments using multiple tasks. The tasks are carefully selected from a task taxonomy, which helps to avoid piecemeal understanding of the effect of the designs. Our design practices and empirical evaluations result in a set of design guidelines on how to choose proper designs according to the characteristics of the tasks and the users. Finally, we demonstrate how to apply the design guidelines to prototype a complex interface for a specific video surveillance application. / Ph. D.

This research study presents two control solutions, PID and the novel hyper-clothoid control strategy, to autonomously navigate a car. These waypoint navigation solutions smoothly connect the given waypoints with C1 continuity using Hermite cubic splines which is used as a reference path for the controller to track. The PID controller uses lateral and heading error to generate a steering profile for the vehicle to track the constructed reference path. A novel real time solution is presented as the second control strategy which involves constructing clothoids to generate a steering profile. The resultant car trajectory preserves curvature and curvature rate continuity. A simulation test bench was developed in MATLAB and Simulink. Six benchmark waypoint datasets have been used for regression testing and validating the algorithms. Both the proposed control strategies have been implemented on a 2017 GM Chevy Bolt EV. A real time operating system (QNX) has been used and was time-synced with the localization suite in the test vehicle. Closed loop results with accuracies up to 50 cm of lateral error have been achieved using the test vehicle. / Doctor of Philosophy / The research into self-driving cars has been one of the most sought out areas these past couple of decades. There are many components into building a self-driving car - Sensing, Perception, Localization, Navigation. Lot of strategies have been developed over the years with waypoint navigation being the most widely used for navigation an autonomous vehicle. Waypoint Navigation utilizes GPS data to move the car from one point to the other. The traditional process of this strategy involves two parts - curve fitting between waypoints and using a control scheme to track the path with the car. Numerous methods have been developed to fit a curve in between two points. Most of these methods use a variant of 3rd degree or higher order polynomials . Also different control strategies have been developed to track the generated path. Model predictive control strategies are among the popular control architectures used for this purpose. This work proposes a novel method to track a path using clothoids. The proposed algorithm has a novel approach of integrating the path construction and control strategy. The algorithm also has a low computational requirement making it highly suitable for implementation in real-time.

Bats are famous for their capability of navigating in dense forests for hundreds of kilometers within one night by using their sonar system. Airborne sonar hasn't been heavily used in the industrial world compared to other sensors such as lidar, radar, and cameras. In this study, we applied a biosonar robot to navigate in a dense forest with bat-like FM-CF ultrasonic signals with deep learning. The results presented show that airborne biosonar can classify different areas' plants, in addition to achieving a similar level of navigation granularity compared to GPS, which is about 6 meters of radius resolution. The time- frequency representations of echoes from the forest are used as input data to explore the biosonar navigation ability, and the state-of-the-art CNN deep network (Resnet 152) is used as the brain to do the echolocation in the dense forest. The navigation ability can be improved significantly by combining multiple 10 ms long echoes, however, the data size of the reflected waves is much smaller than the other popularly used sensors, as echo can be collected at a rate of 40 echoes per second. The results can prove that airborne sonar can be used to navigate in GPS-denied environments, and can be an important sensor used in a scenario when other sensors meet constraints, like in the sensor fusion applications. / Doctor of Philosophy / The ability to identify natural landmarks could contribute to the navigation skills of echolo- cating bats and also advance the quest for autonomy in natural environments with man- made systems. The critical sensors used in autonomous robot navigation are camera array, radar, and lidar, airborne sonar hasn't been verified for its navigation efficiency. However, recognizing natural landmarks based on biosonar echoes has to deal with the unpredictable nature of echoes that are typically superpositions of contributions from many different reflec- tors with unknown properties. This dissertation intends to explore the bioinspired airborne sonar navigation ability in dense natural forests. The first part of this project is to use reflected echoes to navigate on a large scale, data was collected from different mountains which are dozens of kilometers away from each other, and we achieved the use of one single navigator in those locations. The second part is to explore the navigation granularity of airborne sonar sensors, data were collected from a small dense forest area, we try to classify which part of the foliage was based on the echo, and in the end, we achieved GPS accuracy for navigation. The finding in this work proves that the sonar sensor can play an important role in the sensing system, with the help of a deep neural network, with a 10 ms long echo, it can have a similar navigation ability to GPS.

There are two commonly accepted paradigms for organizing intelligence in robotic vehicles, namely reactive and deliberative. A third, a hybrid paradigm called integrated planning and execution, is considered a combination of the original two. Although these paradigms are well known to researchers, there are few published examples directly comparing their application and performance on similar vehicles operating in identical environments. Virginia Tech's participation with two nearly identical vehicles in the DARPA Grand Challenge afforded a practical opportunity for such a case study. Both base vehicles were developed by modifying Club Car Pioneer XRT 1500 on-demand four wheel drive base platforms. Cliff was designed to use the reactive paradigm, while Rocky was designed to use the deliberative paradigm. Both vehicles were initially outfitted with sensor suites and computational capabilities commensurate with the paradigm being employed. The author of this thesis coordinated the activities of the two teams of undergraduate and graduate students who implemented the respective designs and software. Both vehicles proved capable of off-road navigation, including road following and obstacle avoidance in complex desert terrain. In the end, however, the reactive paradigm proved to be smoother and more reliable than the deliberative paradigm under the conditions of our testing. While both vehicles were extensively tested and compared using the competing paradigms, the team modified Rocky to use the more effective reactive paradigm for the Grand Challenge events. The deliberative case shows much promise for complex navigation, but added unnecessary complexity to desert road navigation. This case study, while necessarily limited in scope, may help to shed additional light on the tradeoffs and performance of competing approaches to machine intelligence. / Master of Science