In industrial processes, products are often visually inspected for defects inorder to verify their quality. Many automated visual inspection algorithms exist, and in many cases humans still perform the inspections. Advances in machine learning have showed that deep learning methods lie at the forefront of reliability and accuracy in such inspection tasks. In order to detect defects, most deep learning methods need large amounts of training data to learn from. This makes demonstrating such methods to a new customer problematic, since such data often does not exist beforehand, and has to be gathered specifically for the task. The aim of this thesis is to develop a method to perform such demonstrations. With access to only a small dataset, the method should be able to analyse an image and return a map of binary values, signifying which pixels in the original image belong to a defect and which do not. A method was developed that divides an image into overlapping patches, and analyses each patch individually for defects, using a deep learning method. Three different deep learning methods for classifying the patches were evaluated; a convolutional neural network, a transfer learning model based on the VGG19 network, and an autoencoder. The three methods were first compared in a simple binary classification task, without the patching method. They were then tested together with the patching method on two sets of images. The transfer learning model was able to identify every defect across both tests, having been trained using only four training images, proving that defect detection with deep learning can be done successfully even when there is not much training data available.

Digital image analysis is the field of extracting relevant information from digital images. Recent developments in imaging techniques have made 3-dimensional volume images more common. This has created a need to extend existing 2D image analysis tools to handle images of higher dimensions. Such extensions are usually not straightforward. In many cases, the theoretical and computational complexity of a problem increases dramatically when an extra dimension is added. A fundamental problem in image analysis is image segmentation, i.e., identifying and separating relevant objects and structures in an image. Accurate segmentation is often required for further processing and analysis of the image can be applied. Despite years of active research, general image segmentation is still seen as an unsolved problem. This mainly due to the fact that it is hard to identify objects from image data only. Often, some high-level knowledge about the objects in the image is needed. This high-level knowledge may be provided in different ways. For fully automatic segmentation, the high-level knowledge must be incorporated in the segmentation algorithm itself. In interactive applications, a human user may provide high-level knowledge by guiding the segmentation process in various ways. The aim of the work presented here is to develop segmentation and analysistools for volume images. To limit the scope, the focus has been on two specic capplications of volume image analysis: analysis of volume images of fibrousmaterials and interactive segmentation of medical images. The respective image analysis challenges of these two applications will be discussed. While the work has been focused on these two applications, many of the results presented here are applicable to other image analysis problems.

This thesis introduces a GPU-accelerated algorithm for path planning under kinodynamic constraints, focusing on navigation of flying vehicles within a high-resolution cost map. The algorithm operates by creating dynamically feasible initial paths, and a non-reversible parallel tempering Markov chain Monte Carlo scheme to optimize the paths while adhering to the nonholonomic kinodynamical constraints. The algorithm efficiently generates high quality dynamically feasible paths. An analysis demonstrates the algorithm's robustness, stability and scalability. The approach used for this algorithm is versatile, allowing for straightforward adaptation to different dynamic conditions and cost maps. The algorithm's applicability also extends to various path planning problems, signifying the potential advantages of GPU-accelerated algorithms in the domain of path planning.

MDU, in collaboration with several other universities, plans to join the World Robotic Sailing Championship (WRSC), where in certain sub-challenges some object detection is necessary. Such as for detecting objects such as boats, buoys, and possibly other items. Utilizing a camera system could significantly aid in these tasks, and in this research, an omnidirectional camera is proposed. This is a camera that offers a wide field of view of 360 degrees and could provide comprehensive information about the boat’s surroundings. However, these images use a spherical camera model, which projects the image on a sphere and, when saved to a 2D format, becomes very distorted. To be able to use state-of-the-art vision algorithms for object detection and tracking, this research proposes to project these images to other formats. As such, four systems using object detection and tracking are made that uses different image representation projected from the spherical images. One system uses spherical images and is used as a baseline, while the three remaining systems use some form of projection. The first is cubemap projection, which projects the spherical image to a cube and unfolds this image on a 2D plane. The two other image representations used perspective projections, which are when the spherical image is projected to small sub-images. The two image representations that used perspective projections had 4 and 8 perspective images. None of the systems ultimately performed very well but did have some advantages and disadvantages.

<p>Inefficient traffic operations have far-reaching consequences in not just travel mobility but also public health, social equity, and economic prosperity. Congestion, a key symptom of inefficient operations, can lead to increased emissions, accidents, and productivity loss. Therefore, advancements and policies in transportation engineering require careful scrutiny to not only prevent unintended adverse consequences but also capitalize on opportunities for improvement. In spite of significant efforts to enhance traffic mobility and safety, human control of vehicles remains prone to errors such as inattention, impatience, and intoxication. Connected and autonomous vehicles (CAVs) are seen as a great opportunity to address human-related inefficiencies. This dissertation focuses on connectivity and automation and investigates the synergies between technologies. First, a deep reinforcement learning based strategy is proposed herein to enable agents to address the dynamic nature of inputs in traffic environments and to capture proximal and distant information, and to facilitate learning in rapidly changing traffic. The strategy is applied to alleviate congestion at highway bottlenecks by training a small number of CAVs to cooperatively reduce congestion through deep reinforcement learning. Secondly, to address congestion at intersections, the dissertation introduces a fog-based graphic RL (FG-RL) framework. This approach allows traffic signals across multiple intersections to form a cooperative coalition, sharing information for signal timing prescriptions. Large-scale traffic signal optimization is computationally inefficient, so the proposed FG-RL approach breaks down networks into smaller fog nodes that function as local centralization points within a decentralized system Doing so allows for a bottom-up solution approach for decomposing large traffic networks. Furthermore, the dissertation pioneers the notion of using a small CAV fleet, selected from any existing shared autonomous mobility services (SAMSs) to assist city road agencies to achieve string-stable driving in locally congested urban traffic. These vehicles are dispersed throughout the network to perform their primary function of providing ride-share services. However, when they encounter severe congestion, they act cooperatively with each other to be rerouted and to undertake traffic-stabilizing maneuvers to smoothen the traffic and reduce congestion. The smoothing behavior is learned through DRL, while the rerouting is facilitated through the proposed constrained entropy-based dynamic AV routing algorithm (CEDAR).</p>

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Artificial intelligence and machine learning have seen great progress in recent years. In this work, we will look at the application of machine learning in visual navigational systems for unmanned vehicles in natural environments. Previous works have focused on navigational systems with deep convolutional neural networks (CNNs) for unmanned aerial vehicles (UAVs). In this work, we evaluate the robustness and applicability of these methods for unmanned ground vehicles (UGVs). To evaluate the robustness and applicability of this machine learning approach for UGV two experiments where performed. In the first, data from Swiss trails and photos collected in Swedish forests where used to train deep CNNs. Several models are trained using data collected in different environments at different heights. By cross evaluating the trained models on the other datasets the impact of changing camera position and switching environment can be evaluated. In the second experiment, a navigational system using the trained CNN models were constructed. By evaluating the ability of the system to autonomously follow a forest path an understanding of the applicability of these methods for UGVs in general can be obtained. There where several results from the experiments. When comparing models trained on different datasets, we could see that the environment has an effect on the performance of the navigation, but even more so, the approach is sensitive to the camera position. Finally, an online test to evaluate the applicability of this approach as an end-to-end navigation system for UGVs is done. This experiment showed that these methods, on their own, are not a viable option for an end-to-end navigational system for UGVs in forest environments. / Artificiell intelligens och maskininlärning har gjort stora framsteg de senaste åren. I detta arbete tittar vi på tillämpningen av maskininlärning i visuella navigationssystem för obemannade fordon i naturliga miljöer. Tidigare verk har fokuserat på navigeringssystem med djupa ``convolutional neural networks'' (CNNs) för obemannade luftfarkoster. I detta arbete, utvärderar vi hur pass applicerbara och robusta dessa metoder är som navigationssystem för obemannade markfordon (UGVs). För att utvärdera hur pass applicerbara och robusta dessa maskininlärningsmetoder är för UGVs så utfördes två experiment. I det första experimentet utvärderas hur systemet reagerar på nya miljöer och kamerapositioner. Ett redan existerande dataset, med med foton från stigar i de schweiziska alperna, kompletterade med två nya dataset. Dessa två nya samlingar består av foton från svenska skogsstigar insamlade på två olika höjder. Dessa tre olika dataset användes för att träna tre olika olika modeller. Genom att korsutvärdera de tränade modellerna på de olika dataseten kan effekten av att förändrad kameraposition samt att byta miljö utvärderas. I det andra experimentet utrustades en UGV med ett navigationssystem byggt på dessa tränade modeller. Genom att utvärdering hur pass autonomt denna UGV kan följa en skogsstig så ges en förståelse för hur pass applicerbara dessa metoder är för UGVs generellt. Experimentet gav flera resultat. Korsutvärderingen visade att dessa metoder är känsliga för både kameraposition och miljö. Där byte av kameraposition har en större negativ påverkan på navigationsresultatet, än byte av miljö. Slutligen visade ett online-test att dessa metoder, i sin naiva form, inte är ett lämpligt alternativ för navigationssystem för UGVs i skogsmiljöer.

Path planning that takes kinodynamic constraints into account is a crucial part of critical missions where autonomous vehicles must function independently without communication from an operator as this ensures that the vehicle will be able to follow the planned path. In this thesis, an algorithm is presented that can plan kinodynamically feasible paths through large scale continuous costmap environments for different constraints on the maximum allowed acceleration and jerk along the path. The algorithm begins by taking a small stochastic sample of the costmap, with a higher probability to keep more information from the cheaper, interesting areas of the map. This random sample is turned into a graph to which Dijkstra's algorithm is applied in order to obtain an initial guess of a path. Simulated annealing is then used to first smooth this initial guess to obey the kinodynamic constraints and then optimize the path with respect to cost while keeping the kinodynamics below the set limits. The majority of the simulated annealing iterations utilize a GPU to significantly reduce the computational time needed. The performance of the algorithm was evaluated by studying the paths generated from a large number of different start and end points in a complex continuous costmap with a high resolution of 2551×2216 pixels. To evaluate the robustness of the algorithm a large number of paths were generated, both with the same and with different start and end points, and the paths were inspected both visually, and the spread of the cost of the different paths was studied.  It was concluded that the algorithm is able to generate paths of high quality for different limits on the allowed acceleration and jerk as well as achieving a low spread in cost when generating multiple paths between the same pair of points. The utilization of a GPU to improve computational performance proved successful as the GPU executed between 2.4 and 2.8 times more simulated annealing iterations in a given time compared to the CPU. This result hopefully inspires future work to utilize GPUs to improve computational performance, even in problems that traditionally are solved using sequential algorithms.

The need to further unlock productivity of forestry operations urges the increase of forestry automation. Many essential operations in forest production, such as harvesting, forwarding, planting, etc., have the potential to be automated and obtain benefits such as improved production efficiency, reduced operating costs, and an improved working environment. In view of the fact that forestry operations are performed in forest environments, the automation of forestry operations is thus complex and extremely challenging. To build the ability of forest machine automation, it is necessary to construct an environmental cognitive ability of the forest machine as basis. Through a combination of exteroceptive sensors and algorithms, forest machine vision can be realized. Using new and off-the-shelf solutions for detecting, locating, classifying and analyzing the status of objects of concern surrounding the machine during forestry operations in combination with smart judgement and control, forest operations can be automated. This thesis focuses on the introduction of vision systems on an unmanned forest platform, aiming to create the foundation for autonomous decision-making and execution in forestry operations. Initially, the vision system is designed to work on an unmanned forest machine platform, to create necessary conditions to either assist operators or to realize automatic operation as a further step. In this thesis, vision systems based on stereo camera sensing are designed and deployed on an unmanned forest machine platform and the functions of detection, localization and pose estimation of objects that surround the machine are developed and evaluated. These mainly include a positioning function for forest terrain obstacles such as stones and stumps based on stereo camera data and deep learning, and a localization and pose estimation function for ground logs based on stereo camera and deep learning with added functionality of color difference comparison. By testing these systems’ performance in realistic scenarios, this thesis describe the feasibility of improving the automation level of forest machine operation by building a vision system. In addition, the thesis also demonstrate that the accuracy of stump detection can be improved without significantly increasing the processing load by introducing depth information into training and execution.