This thesis presents a 3D reconstruction approach to the detection of static obstacles from a single rear view parking camera. Corner features are tracked to estimate the vehicle’s motion and to perform multiview triangulation in order to reconstruct the scene. We model the camera motion as planar motion and use the knowledge of the camera pose to efficiently solve motion parameters. Based on the observed motion, we selected snapshots from which the scene is reconstructed. These snapshots guarantee a sufficient baseline between the images and result in more robust scene modeling. Multiview triangulation of a feature is performed only if the feature obeys the epipolar constraint. Triangulated features are semantically labelled according to their 3D location. Obstacle features are spatially clustered to reduce false detections. Finally, the distance to the nearest obstacle cluster is reported to the driver.

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Doctor of Philosophy / Department of Mathematics / Ivan Blank / We study the obstacle problem with an elliptic operator in nondivergence form with principal coefficients in VMO. We develop all of the basic theory of existence, uniqueness, optimal regularity, and nondegeneracy of the solutions. These results, in turn, allow us to begin the study of the regularity of the free boundary, and we show existence of blowup limits, a basic measure stability result, and a measure-theoretic version of the Caffarelli alternative proven in Caffarelli's 1977 paper ``The regularity of free boundaries in higher dimensions." Finally, we show that blowup limits are in general not unique at free boundary points.

Many mobility impaired users are unable to operate a powered wheelchair safely, without causing harm to themselves, others, and the environment. Smart wheelchairs that assist or replace user control have been developed to cater for these users, utilising systems and algorithms from autonomous robots. Despite a sustained period of research and development of robotic wheelchairs, there are very few available commercially. This thesis describes work towards developing a navigation system that is aimed at being retro-fitted to powered wheelchairs. The navigation system developed takes a systems engineering approach, integrating many existing open-source software projects to deliver a system that would otherwise not be possible in the time frame of a master's thesis. The navigation system introduced in this thesis is aimed at operating in an unstructured indoor environment, and requires no a priori information about the environment. The key components in the system are: obstacle avoidance, map building, localisation, path planning, and autonomously travelling towards a goal. The test electric wheelchair was instrumented with the following: a laptop, a laser scanner, wheel encoders, camera, and a variety of user input methods. The user interfaces that have been implemented and tested include a touch screen friendly graphical user interface, keyboard and joystick.

We adress an obstacle problem for (graphical) mean curvature flow with Dirichlet boundary conditions. Using (an adapted form of) the standard implicit time-discretization scheme we derive the existence of distributional solutions satisfying an appropriate variational inequality. Uniqueness of this flow and asymptotic convergence towards the stationary solution is proven.

Este trabalho apresenta um sistema de rastreamento de obstáculos, utilizando sensor laser 2D e filtro de Kalman. Este filtro não é muito eficiente em situações em que ocorrem severas perturbações na posição medida do obstáculo, como, por exemplo, um objeto rastreado passando por trás de uma barreira, interrompendo o feixe de laser por alguns instantes, tornando impossível receber do sensor as informações sobre sua posição. Este trabalho sugere um método de minimizar esse problema com o uso de um algoritmo denominado Corretor de Discrepâncias. / An obstacle detection and tracking system using a 2D laser sensor and the Kalman filter is presented. This filter is not very efficient in case of severe disturbances in the measured position of the obstacle, as for instance, when an object being tracked is behind a barrier, thus interrupting the laser beam, making it impossible to receive the sensor information about its position. This work suggests a method to minimize this problem by using an algorithm called Corrector of Discrepancies.

Navigation and guidance of mobile robots towards steady or maneuvering objects (targets) is one of the most important areas of robotics that has attracted a lot of attention in recent decades. However, in most of the existing methods, both the line-of-sight angle (bearing) and the relative distance (range) are assumed to be available for navigation and guidance algorithms. There is also a relatively large body of research on navigation and guidance with bearings-only measurements. In contrast, only a few results on navigation and guidance towards an unknown target using range-only measurements have been published. Various problems of navigation, guidance, location estimation and target tracking based on range-only measurements often arise in new wireless networks related applications. Recent advances in these applications allow us to use inexpensive transponders and receivers for range-only measurements which provide information in dynamic and noisy environments without the necessity of line-of-sight. To take advantage of these sensors, algorithms must be developed for range-only navigation. The main part of this thesis is concerned with the problem of real-time navigation and guidance of Wheeled Mobile Robots (WMRs) towards an unknown stationary or moving target using range-only measurements. The range can be estimated using the signal strength and the robust extended Kalman filtering. Several similar algorithms for navigation and guidance termed Equiangular Navigation and Guidance (ENG) laws are proposed and mathematically rigorous proofs of convergence and stability of the proposed guidance laws are given. The experimental investigation into the use of range data for a WMR navigation is documented and the results and discussions on the performance of the proposed guidance strategies are presented, where a wheeled robot successfully approach a stationary or follow a maneuvering target. In order to safely navigate and reliably operate in populated environments, ENG is then modified into Augmented-ENG (AENG), which enables the robot to approach a stationary target or follow an unpredictable maneuvering object in an unknown environment, while keeping a safe distance from the target, and simultaneously preserving a safety margin from the obstacles. Furthermore, we propose and experimentally investigate a new biologically inspired method for local obstacle avoidance and give the mathematically rigorous proof of the idea. In order for the robot to avoid collision and bypass the enroute obstacles in this method, the angle between the instantaneous moving direction of the robot and a reference point on the surface of the obstacle is kept constant. The proposed idea is combined with the ENG law, which leads to a reliable and fast long-range navigation. The performance of both navigation strategy and local obstacle avoidance techniques are confirmed with computer simulations and several experiments with ActivMedia Pioneer 3-DX wheeled robots. The second part of the thesis investigates some challenging problems in the area of wheeled robot navigation. We first address the problem of bearing-only guidance of an autonomous vehicle following a moving target with smaller minimum turning radius compared to that of the follower and propose a simple and constructive navigation law. In compliance with the increasing research on decentralized control laws for groups of mobile autonomous robots, we consider the problems of decentralized navigation of network of WMRs with limited communication and decentralized stabilization of formation of WMRs. New control laws are presented and simulation results are provided to illustrate the control laws and their applications.

<p>This thesis describes an ongoing project of which the purpose is designing and developing techniques and algorithms for autonomous off-road vehicles. The focus is on some of the components necessary to accomplish autonomous navigation, which involves sensing and moving safely along a user-defined path in a dynamic forest environment. The work is part of a long-term vision in the forest industry of developing an unmanned shuttle that transports timber from the felling area to the main roads for further transportation. A new path-tracking algorithm is introduced and demonstrated as superior to standard algorithms, such as Follow the Carrot and Pure Pursuit. This is accomplished by using recorded data from a path-learning phase. By using the recorded steering angle, the curvature of the path is automatically included in the final steering command. Localization is accomplished by a neural network that fuses data from a Real-Time Kinematic Differential GPS/GLONASS, a gyro, and wheel odometry. Test results are presented for path tracking and localization accuracy from runs conducted on a full-sized forest machine. A large part of the work has been design and implementation of a general software platform for research in autonomous vehicles. The developed algorithms and software have been implemented and tested on a full-size forest machine supplied by our industrial partner Komatsu Forest AB. Results from successful field tests with autonomous path tracking, including obstacle avoidance, are presented.</p>

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