Two areas of expertise required in the production of industrial and commercial robotics are motor control and obstacle navigation algorithms. This is especially true in the field of autonomous robotic vehicles, and this application will be the focus of this work. This work is divided into two parts. Part I describes the motor types and feedback devices available and the appropriate choice for a given robotics application. This is followed by a description of the control strategies available and appropriate for a variety of situations. Part II describes the vision hardware and navigation software necessary for an autonomous robotic vehicle. The conclusion discusses how the two parts are coming together in the emerging field of electric smart car technology. The content is aimed at the robotic vehicle designer. Both parts present a contribution to the field but also survey the required background material for a researcher to enter into development. The material has been made succinct and graphical wherever appropriate. / Master of Science

Navigation of autonomous underwater vehicles (AUVs) in the subsea environment is particularly challenging due to the unavailability of GPS because of rapid attenuation of electromagnetic waves in water. As a result, the AUV requires alternative methods for position estimation. This thesis describes a terrain-aided navigation approach for an AUV where, with the help of a prior depth map, the AUV localizes itself using altitude measurements from a multibeam DVL. The AUV simultaneously builds a probabilistic depth map of the seafloor as it moves to unmapped locations. The main contribution of this thesis is a new, scalable, and on-line terrain-aided navigation solution for AUVs which does not require the assistance of a support surface vessel. Simulation results on synthetic data and experimental results from AUV field trials in Panama City, Florida are also presented. / Master of Science / Navigation of autonomous underwater vehicles (AUVs) in subsea environment is particularly challenging due to the unavailability of GPS because of rapid attenuation of electromagnetic waves in water. As a result, the AUV requires alternative methods for position estimation. This thesis describes a terrain-aided navigation approach for an AUV where, with the help of a prior depth map, the AUV localizes itself using altitude measurements from a multibeam DVL. The AUV simultaneously builds a probabilistic depth map of the seafloor as it moves to unmapped locations. The main contribution of this thesis is a new, scalable, and on-line terrain-aided navigation solution for AUVs which does not require assistance of a support surface vessel. Simulation results on synthetic data and experimental results from AUV field trials in Panama City, Florida are also presented.

Globally, over 2.2 billion people face vision impairment, necessitating innovative solutions for safe, independent navigation. Traditional aids like canes, guide dogs, and GPS offer basic support but lack the sophistication to provide contextual understanding, precise navigation, or real-time hazard alerts. This project presents SmartGuide, a mobile app designed to enhance the independence of visually impaired users through AI-driven features. SmartGuide offers three main functions: (1) Smart Vision, using the GPT-4 Vision API to deliver spoken feedback about surroundings; (2) Navigation, combining QR code detection via YOLO with ZoeDepth for depth estimation, guiding users to destinations through the shortest path calculated by Dijkstra's algorithm; and (3) Obstacle Detection and Alerts, where YOLO identifies obstacles, and ZoeDepth estimates their distance to inform users of potential hazards. By adapting its responses based on user feedback, SmartGuide provides personalized, reliable guidance that empowers visually impaired individuals to navigate with confidence and safety, advancing the field of accessible technology. / Master of Science / Navigating unfamiliar or crowded spaces is a major challenge for visually impaired individuals, who often rely on canes, guide dogs, or GPS tools. While useful, these aids offer only limited guidance and do not provide detailed information about surroundings, directions, or nearby obstacles. SmartGuide is a mobile application designed to address these gaps, enabling visually impaired users to navigate more independently and safely. SmartGuide includes three main features: Smart Vision, which gives audio feedback about the user's surroundings; Navigation, which uses QR codes and depth estimation to guide users to destinations along the shortest path; and Obstacle Alerts that detects obstacles, warning users of potential hazards. Using advanced AI technologies and feedback from visually impaired users, SmartGuide delivers clear, actionable guidance that supports confident, safe movement in both familiar and new environments. This research-driven tool demonstrates how technology can enhance accessibility, making navigation easier and safer for those with vision impairment.

Janet Taylor made major contributions to Victorian navigational practices. She did so through creating business opportunities for herself as an educator, author, and inventor of nautical instruments. / Master of Arts / Janet Taylor, a woman who made major contributions to Victorian navigation, is representative of a large historiographical gap in maritime and nautical histories. In these fields historians are typically inclined to look at famous men in navigation: John Hadley, John Campbell, and others who invented nautical instruments such as the octant and sextant. However, we have failed to contextualize the significant women who have innovated maritime practices throughout history. Taylor, for example, adjusted calculations for locating positions at sea according to the realization that the shape of the earth is not spherical, but spheroidal. She conveyed this new mathematical principle to the maritime community of London through the classes she taught at her nautical academies, the dozens of books she would publish, and the navigational tools she invented or innovated. Her multiple careers, and her success in each of them, were varied and far-reaching, making her truly a Jane of all trades. Her success as a woman in a male-dominated field was largely dependent on the industrial spirit of the nation and time in which she lived. As the industrial revolution created a need for advancement in technology and navigation, gender norms and the public/private dichotomy of Victorian England began to blur.

Siblings are the longest lasting relationships most individuals may experience in their life. What makes sibling relationships unique is the overlap of both shared and unshared experiences. While there is limited research on the mechanisms behind sibling relationships in general, research on Latino/a/x sibling relationships is even more limited. The limited research on Latino/a/x siblings from immigrant families has found they have an impact on each other's cultural adaptations. The current study explored the influence of the acculturation cultural adaptation processes to the U.S. and how this adaptation may impact Latino/a/x sibling relationships. Semi-structured dyadic interviews were conducted with eight sibling dyads (N = 8) and dyadic analysis methods from Tkachuk et al. (2019) were used to analyze the qualitative data. The findings suggest that the sibling relationship is influenced by parental and cultural expectations, unique experiences pertaining to growing up (e.g., sibling positionality), and their shared experiences of growing up in the United States (i.e., shared cultural navigation). Findings regarding the importance of family are congruent with current literature on Latino/a/x immigrant families and a new finding that emerged related to the validation of younger siblings on the experiences of the older siblings. Clinical implications suggest clinicians familiarizing themselves with cross-cultural sibling relationships and the benefits of having siblings in therapy. Limitations and recommendations for future study are discussed. / Master of Science / The Latino/a/x population is among the fastest growing immigrant populations in the United States. There is vast research on immigrant families that emphasize the acculturation processes as crucial to understanding how these families blend multiple cultures. Most of the research of acculturation and its influence on familial relationships focus on parent-child relationships and limited research has been done on the influence of this phenomenon on other family relationships, including sibling relationships. Siblings relationships can be the longest lasting relationship an individual can have in their lifetime and what makes this relationship unique is the overlap of both shared and unshared experiences. The limited research on Latino/a/x siblings from immigrant families has found that siblings have an impact on each other's cultural adaptations. The current study explored the influence of the acculturation cultural adaptation processes to the U.S. and how this adaptation may impact Latino/a/x sibling relationships. Semi-structured dyadic interviews were conducted with eight sibling dyads (N = 8) and dyadic analysis methods from Tkachuk et al. (2019) were used to analyze the qualitative data. The study's findings highlight that the sibling relationship is influenced by parental and cultural expectations, unique experiences pertaining to growing up (e.g., sibling positionality), and their shared experiences of growing up in the United States (i.e., shared cultural navigation). Given these findings, clinical implications suggest clinicians familiarizing themselves with cross-cultural sibling relationships and the benefits of having siblings in therapy. Limitations and recommendations for future study are discussed.

The testing of unmanned vehicles presents a need for an independent device capable of accurately collecting position and orientation data. While commercial-off-the-shelf components could be pieced together to sense and record this information, this is an expensive, large, and heavy solution, not suitable for small or aerial vehicles. The micro wireless instrumented payload, or μWIP, was designed precisely for this purpose. The μWIP includes a GPS receiver, 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer which are used to measure an unmanned vehicle's position and orientation. The device also uses a secure digital card for data storage, and an 802.11b module to provide wireless connectivity. Additionally, the μWIP contains a on-board battery and the circuitry required to charge it. Firmware for the ARM7 processor was written to allow sensor calibration and data transmission, and a user interface was designed to run on a personal computer. The finished design is a tiny 3''x5''x1'', and weighs a mere 0.8 pounds including battery and antennas. It is capable of continuously streaming accurate GPS and inertial data over an 802.11b wireless network for over 5 hours. Having a bill of materials cost just over $600, the μWIP is also more cost effective than any alternative solutions. This thesis details the hardware and software design of the μWIP, as well as the initial testing, calibration, and evaluation of the device. / Master of Science

This thesis focuses on the theory and implementation of visual navigation techniques for Autonomous Air Vehicles in outdoor environments. The target of this study is to fuse and cooperatively develop an incremental map for multiple air vehicles under the application of Simultaneous Location and Mapping (SLAM). Without loss of generality, two unmanned air vehicles (UAVs) are investigated for the generation of ground maps from current and a priori data. Each individual UAV is equipped with inertial navigation systems and external sensitive elements which can provide the possible mixture of visible, thermal infrared (IR) image sensors, with a special emphasis on the stereo digital cameras. The corresponding stereopsis is able to provide the crucial three-dimensional (3-D) measurements. Therefore, the visual aerial navigation problems tacked here are interpreted as stereo vision based SLAM (vSLAM) for both single and multiple UAVs applications. To begin with, the investigation is devoted to the methodologies of feature extraction. Potential landmarks are selected from airborne camera images as distinctive points identified in the images are the prerequisite for the rest. Feasible feature extraction algorithms have large influence over feature matching/association in 3-D mapping. To this end, effective variants of scale-invariant feature transform (SIFT) algorithms are employed to conduct comprehensive experiments on feature extraction for both visible and infrared aerial images. As the UAV is quite often in an uncertain location within complex and cluttered environments, dense and blurred images are practically inevitable. Thus, it becomes a challenge to find feature correspondences, which involves feature matching between 1st and 2nd image in the same frame, and data association of mapped landmarks and camera measurements. A number of tests with different techniques are conducted by incorporating the idea of graph theory and graph matching. The novel approaches, which could be tagged as classification and hypergraph transformation (HGTM) based respectively, have been proposed to solve the data association in stereo vision based navigation. These strategies are then utilised and investigated for UAV application within SLAM so as to achieve robust matching/association in highly cluttered environments. The unknown nonlinearities in the system model, including noise would introduce undesirable INS drift and errors. Therefore, appropriate appraisals on the pros and cons of various potential data filtering algorithms to resolve this issue are undertaken in order to meet the specific requirements of the applications. These filters within visual SLAM were put under investigation for data filtering and fusion of both single and cooperative navigation. Hence updated information required for construction and maintenance of a globally consistent map can be provided by using a suitable algorithm with the compromise between computational accuracy and intensity imposed by the increasing map size. The research provides an overview of the feasible filters, such as extended Kalman Filter, extended Information Filter, unscented Kalman Filter and unscented H Infinity Filter. As visual intuition always plays an important role for humans to recognise objects, research on 3-D mapping in textures is conducted in order to fulfil the purpose of both statistical and visual analysis for aerial navigation. Various techniques are proposed to smooth texture and minimise mosaicing errors during the reconstruction of 3-D textured maps with vSLAM for UAVs. Finally, with covariance intersection (CI) techniques adopted on multiple sensors, various cooperative and data fusion strategies are introduced for the distributed and decentralised UAVs for Cooperative vSLAM (C-vSLAM). Together with the complex structure of high nonlinear system models that reside in cooperative platforms, the robustness and accuracy of the estimations in collaborative mapping and location are achieved through HGTM association and communication strategies. Data fusion among UAVs and estimation for visual navigation via SLAM were impressively verified and validated in conditions of both simulation and real data sets.

International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California / A recurring problem in flight testing navigation systems is the need for an accurate, common time reference for the system under test and for the truth source to which it is compared. Lockheed Martin Aeronautics Company and Computer Sciences Corporation have developed software that utilizes all available timing information to reference the times of validity for each navigation measurement to Coordinated Universal Time. This permits accurate comparison and correlation of data necessary for statistical error analysis of the navigation system.

Robust long-term positioning for autonomous mobile robots is essential for many applications. In many environments this task is challenging, as errors accumulate in the robot’s position estimate over time. The robot must also build a map so that these errors can be corrected when mapped regions are re-visited; this is known as Simultaneous Localisation and Mapping, or SLAM. Successful SLAM schemes have been demonstrated which accurately map tracks of tens of kilometres, however these schemes rely on expensive sensors such as laser scanners and inertial measurement units. A more attractive, low-cost sensor is a digital camera, which captures images that can be used to recognise where the robot is, and to incrementally position the robot as it moves. SLAM using a single camera is challenging however, and many contemporary schemes suffer complete failure in dynamic or featureless environments, or during erratic camera motion. An additional problem, known as scale drift, is that cameras do not directly measure the scale of the environment, and errors in relative scale accumulate over time, introducing errors into the robot’s speed and position estimates. Key to a successful visual SLAM system is the ability to continue operation despite these difficulties, and to recover from positioning failure when it occurs. This thesis describes the development of such a scheme, which is known as BoWSLAM. BoWSLAM enables a robot to reliably navigate and map previously unknown environments, in real-time, using only a single camera. In order to position a camera in visually challenging environments, BoWSLAM combines contemporary visual SLAM techniques with four new components. Firstly, a new Bag-of-Words (BoW) scheme is developed, which allows a robot to recognise places it has visited previously, without any prior knowledge of its environment. This BoW scheme is also used to select the best set of frames to reconstruct positions from, and to find efficient wide-baseline correspondences between many pairs of frames. Secondly, BaySAC, a new outlier- robust relative pose estimation scheme based on the popular RANSAC framework, is developed. BaySAC allows the efficient computation of multiple position hypotheses for each frame. Thirdly, a graph-based representation of these position hypotheses is proposed, which enables the selection of only reliable position estimates in the presence of gross outliers. Fourthly, as the robot explores, objects in the world are recognised and measured. These measurements enable scale drift to be corrected. BoWSLAM is demonstrated mapping a 25 minute 2.5km trajectory through a challenging and dynamic outdoor environment in real-time, and without any other sensor input; considerably further than previous single camera SLAM schemes.

Unmanned ground mobile robots are land-based robots which do not have a human passenger on board. They can be either autonomous, or controlled via telecommunication. For navigational purposes, GPS is often used. However, the GPS signal can be distorted in obstructive environments such as tunnels, urban canyons, and dense forests. IMUs can be used to provide an internal navigational solution, free from external input. However, low cost IMUs are prone to various intrinsic sources of error, which leads to large errors in the long run. Using the short term accuracy of the IMU, and the long term accuracy of the GPS, these two technologies are often integrated to combine the aforementioned aspects of the two systems. For integration of the two, various methods are implemented. Such integration methods include Particle Filters, and Kalman Filters. Kalman Filters are commonly used due to their simplicity in calculations. However, the Kalman Filter linearizes the nonlinear error estimates which are inherent with low cost IMUs. The Kalman Filter also does not account for IMU measurement drift, which is present when the measurement unit is used for a long period of time. In this thesis, a Parallel Cascade Identification (PCI) algorithm is augmented with the Kalman Filter (KF) to model the nonlinear errors which are intrinsic to the low cost IMU. The method of integration used was 2D GPS/RISS loosely coupled integration using a Kalman Filter. The PCI algorithm modelled the nonlinear error for the z-axis gyroscope while the GPS signal was available. During a GPS outage, the PCI nonlinear error model was combined with the KF estimated error and the mechanization error, to provide a corrected azimuth. The KFPCI algorithm showed an improvement over the KF algorithm in RMS position error, maximum position error, RMS azimuth error, and maximum azimuth error by an average of 30.76%, 34.71%, 66.76%, and 53.58% in each of the respective areas. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2013-06-11 18:13:12.625