A growing number of indjviduals in this country, who share a common background, are gaining academic success, accessing work-related training, further and higher education, and entering rewarding occupations. Their common background is home-schooling. They have received all or part of their 'compulsory' years of education at home. This is a study of a small number of such individuals aged sixteen and over. I include case studies of their family's home-schooling experiences and focus on the legacy that this 'alternative' education has provided for their transition into 'institutionalised' learning - at school, college, university or workplace. I explore political, historical and philosophical perspectives of the 'movement', which has escalated rapidly in the USA over the past twenty years and is gaining impetus in the UK. Views expressed by Education Authority officers, teachers and employers also help me to frame my inquiry into whether this educational provision presents an appropriate rather than (as I initially viewed it ) a radical alternative to school. As well as revealing the development of my understanding of this multifaceted, alternative form of 'child-centred' education, this study also examines the methodological complexities of the inquiry.

This master thesis contains modeling and nonlinear automatic control of an autonomous lawn mower. The vehicle shall be able to follow a magnetic field loop buried in the lawn on an arbitrary distance, including straddling. A unicycle model of the lawn mower has been derived based on previous work. The model holds for the assumption of non slip environment. Two diff erent control strategies have been implemented and evaluated in this thesis. One is based on feedback linearization  and with the feedback gain estimated using an LQ-controller. The other strategy is based on linearization at an equilibrium point  and also with the use of an LQ-controller. The only state that can be measured is the distance to the magnetic loop. In order to estimate the other states, an observer has been implemented using the linearized in an equilibrium point  model and a Kalman filter. The navigation of the autonomous lawn mower is performed using a magnetic loop buried in the lawn. The non-linearity of the magnetic field causes diffi culties when estimating the distance to the loop. One magnetic field strength corresponds to two distances to the magnetic loop. The nature of the magnetic field and the measurement noise causes the precision of the controller to be limited. The conclusion of this master thesis is that it is possible to navigate and follow the magnetic loop at a distance greater than 0.3 [m], but with overshoots during sharp turns. If the reference distance is smaller than that, the behavior of the robot will be oscillating when following the loop. The limitations of the controller performance is due to the lack of information which can be extracted from the measurements of the magnetic field.

International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California / There is an increasing demand for practical and powerful navigation system to lead people from one place to another quickly and rightly. The introduction of a new embedded operating system, Windows CE, allows us to design a compact, low-cost, efficient autonomous vehicle location and navigation system. This paper discusses the advantages of Windows CE, demonstrates the possibility of building an AVLNS based on it. Then a realization scheme of hardware platform and navigation software is presented.

ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV / In this paper, we present our research on using computer-vision analysis for vehicular safety applications. Our research has potential applications for both autonomous vehicles and connected vehicles. In particular, for connected vehicles, we propose three image analysis algorithms that enhance the quality of a vehicle's on-board video before inter-vehicular information exchange takes place. For autonomous vehicles, we are investigating a visual analysis scheme for collision avoidance during back up and an algorithm for automated 3D map building. These algorithms are relevant to the telemetering domain as they involve determining the relative pose between a vehicle and other vehicles on the road, or between a vehicle and its 3D driving environment, or between a vehicle and obstacles surrounding the vehicle.

The purpose of this research is to develop a generalised model for levels of autonomy and sophistication for autonomous systems. It begins with an introduction to the research, its aims and objectives before a detailed review of related literature is presented as it pertains to the subject matter and the methodology used in the research. The research tasks are carried out using appropriate methods including literature reviews, case studies and semi-structured interviews. Through identifying the gaps in the current work on human assistive robots, a generalised model for assessing levels of autonomy and sophistication for human assistive robots (ALFHAR) is created through logical modelling, semi-structured interview methods and case studies. A web-based tool for the ALFHAR model is also created to support the model application. The ALFHAR model evaluates levels of autonomy and sophistication with regard to the decision making, interaction, and mechanical ability aspects of human assistive robots. The verification of the model is achieved by analysing evaluation results from the web-based tool and ALFHAR model. The model is validated using a set of tests with stakeholders participation through the conduction of a case study using the web-based tool. The main finding from this research is that the ALFHAR model can be considered as a model to be used in the evaluation of levels of autonomy and sophistication for human assistive robots. It can also prove helpful as part of through life management support for autonomous systems. The thesis concludes with a critical review of the research and some recommendations for further research.

Fully automated or autonomous vehicles (AVs) hold great promise for the future of transportation, with Google and other auto manufacturers intending on introducing self-driving cars to the public by 2020. New automation functionalities will produce dramatic transportation system changes, across safety, mobility, travel behavior, and the built environment. This work’s results indicate that AVs may save the U.S. economy up to $37.7 billion from safety, mobility and parking improvements at the 10% market penetration level (in terms of system-wide vehicle-miles traveled [VMT]), and up to $447.1 billion with 90% market penetration. With only 10% market share, over 1,000 lives could be saved annually. However, realizing these potential benefits while avoiding pitfalls requires overcoming significant barriers including AV costs, liability, security, privacy, and missing research. Additionally, once fully self-driving vehicles can safely and legally drive unoccupied, a new personal travel transportation mode looks set to arrive. This new mode is the shared automated vehicle (SAV), combining on-demand service features with self-driving capabilities. This work simulates a fleet of SAVs operating within Austin, Texas, first using an idealized grid-based representation, and next using Austin’s actual transportation network and travel demand flows. This second model incorporates dynamic ride-sharing (DRS), allowing two or more travelers with similar origins, destinations and departure times to share a ride. Model results indicate that each SAV could replace around 10 conventionally-owned household vehicles while serving over 56,000 person-trips. SAVs’ ability to relocate unoccupied between serving one traveler and the next may cause an increase of 7-10% more travel; however, DRS can result in reduced overall VMT, given enough SAV-using travelers willing to ride-share. Furthermore, using DRS results in overall lower wait and service times for travelers, particularly from pooling rides during peak demand. SAVs should produce favorable emissions outcomes, with an estimated 16% less energy use and 48% lower volatile organic compound (VOC) emissions, per person-trip compared to conventional vehicles. Finally, assuming SAVs cost $70,000 each, an SAV fleet in Austin could provide a 19% return on investment, when charging $1 per trip-mile served. In summary, this new paradigm holds much promise that technological advances may soon realized. / text

To hasten the process of saving lives after disasters in urban areas, autonomous robots are being looked to for providing mapping, hazard identification and casualty location. These robots need to maximise time in the field without having to recharge and without reducing productivity. This project aims to improve autonomous robot navigation through allowing comparison of algorithms with various weightings, in conjunction with the ability to vary physical parameters of the robot and other factors such as error thresholds/limits. The lack of a priori terrain data in disaster sites, means that robots have to dynamically create a representation of the terrain from received sensor range-data in order to path plan. To reduce the resources used, the affect of input data on the terrain model is analysed such that some points may be culled. The issues of identifying hazards within these models are considered with respect to the effect on safe navigation. A modular open-source platform has been created which allows the automated running of experimental trials in conjunction with the implementation and use of other input types, node networks, or algorithms. Varying the terrains, obstacles, initial positions and goals, which a virtual robot is tasked with navigating means that the design, and hence performance, are not tailored to individual situations. Additionally, this demonstrates the variability of scenarios possible. This combination of features allows one to identify the effects of different design decisions, while the use of a game-like graphical interface allows users to readily view and comprehend the scenarios the robot encounters and the paths produced to traverse these environments. The initially planned focus of experimentation lay in testing different algorithms and various weightings, however this was expanded to include different implementations and factors of the input collection, terrain modelling and robot movement. Across a variety of terrain scenarios, the resultant paths and status upon trial completion were analysed and displayed to allow observations to be made. It was found that the path planning algorithms are of less import than initially believed, with other facets of the robotic system having equally significant roles in producing quality paths through a hazardous environment. For fixed view robots, like the choice used in this simulator, it was found that there were issues of incompatibility with A* based algorithms, as the algorithm’s expected knowledge of the areas in all directions regardless of present orientation, and hence they did not perform as they are intended. It is suggested that the behaviour of such algorithms be modified if they are to be used with fixed view systems, in order to gather sufficient data from the surroundings to operate correctly and find paths in difficult terrains. A simulation tool such as this, enables the process of design and testing to be completed with greater ease, and if one can restrain the number of parameters varied, then also with more haste. These benefits will make this simulation tool a valuable addition to the field of USAR research.

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This thesis addresses the problem of intelligent control of autonomous mobile robots, particularly under circumstances unforeseen by the designer. As the range of applications for autonomous robots widens and increasingly includes operation in unknown environments (exploration) and tasks which are not clearly specifiable a priori (maintenance work), this question is becoming more and more important. It is argued that in order to achieve such flexibility in unforeseen situations it is necessary to equip a mobile robot with the ability to autonomously acquire the necessary task achieving competences, through interaction with the world. Using mobile robots equipped with self-organising, behaviour-based controllers,experiments in the autonomous acquisition of motor competences and navigational skills were conducted to investigate the viability of this approach. A controller architecture is presented that allows extremely fast acquisition of motor competence such as obstacle avoidance, wall and corridor following and deadend escape: these skills are obtained in less than five learning steps,performed in under one minute of real time. This is considerably faster than previous approaches. Because the effective wiring between sensors and actuators is determined autonomously by the robot, sensors and actuators may initially be wired up arbitrarily,which reduces the risk of human error during the setting up phase of the robot. For the first time it was demonstrated that robots also become able to autonomously recover from unforeseen situations such as changes in the robot's morphology, the environment or the task. Rule-based approaches to error recovery obviously cannot offer recovery from unforeseen errors,as error situations covered by such approaches have to be identified beforehand. A robust and fast map building architecture is presented that enables mobile robots to autonomously construct internal representations of their environment, using self-organising feature maps. After a short training time the robots are able to use these self-organising feature maps successfully for location recognition. For the first time the staged acquisition of multiple competences in mobile robots is presented. First obtaining fundamental motor competences such as wall following and deadend escape (primary skills), the robots use these in a second stage to learn higher levels of competence such as the navigational task of location recognition (secondary skills). Besides laying the foundation of autonomous, staged acquisition of high level competences, this approach has the interesting property of securely grounding secondary skills in the robot's own experience, as these secondary skills are defined in terms of the primary ones.