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Full-Text Articles in Engineering
System Development Of An Unmanned Ground Vehicle And Implementation Of An Autonomous Navigation Module In A Mine Environment, Jonas Amoama Bredu Jnr
System Development Of An Unmanned Ground Vehicle And Implementation Of An Autonomous Navigation Module In A Mine Environment, Jonas Amoama Bredu Jnr
Graduate Theses, Dissertations, and Problem Reports
There are numerous benefits to the insights gained from the exploration and exploitation of underground mines. There are also great risks and challenges involved, such as accidents that have claimed many lives. To avoid these accidents, inspections of the large mines were carried out by the miners, which is not always economically feasible and puts the safety of the inspectors at risk. Despite the progress in the development of robotic systems, autonomous navigation, localization and mapping algorithms, these environments remain particularly demanding for these systems. The successful implementation of the autonomous unmanned system will allow mine workers to autonomously determine …
Improving Robotic Decision-Making In Unmodeled Situations, Nicholas Scott Ohi
Improving Robotic Decision-Making In Unmodeled Situations, Nicholas Scott Ohi
Graduate Theses, Dissertations, and Problem Reports
Existing methods of autonomous robotic decision-making are often fragile when faced with inaccurate or incompletely modeled distributions of uncertainty, also known as ambiguity. While decision-making under ambiguity is a field of study that has been gaining interest, many existing methods tend to be computationally challenging, require many assumptions about the nature of the problem, and often require much prior knowledge. Therefore, they do not scale well to complex real-world problems where fulfilling all of these requirements is often impractical if not impossible. The research described in this dissertation investigates novel approaches to robotic decision-making strategies which are resilient to …
Multimodal Adversarial Learning, Uche Osahor
Multimodal Adversarial Learning, Uche Osahor
Graduate Theses, Dissertations, and Problem Reports
Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition, generative modelling, and multi-modal learning in various computer vision applications. However, recent findings have shown that such state-of-the-art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input. A good target detection systems can accurately identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. However, prior research still confirms that such state of the art targets models …