The use of UAVs for remote sensing is increasing. In this paper, we demonstrate a method for evaluating and selecting suitable hardware to be used for deployment of algorithms for UAV-based remote sensing under considerations of Size, Weight, Power, and Computational constraints. These constraints hinder the deployment of rapidly evolving computer vision and robotics algorithms on UAVs, because they require intricate knowledge about the system and architecture to allow for effective implementation. We propose integrating computational monitoring techniques—profiling—with an industry standard specifying software quality—ISO 25000—and fusing both in a decision-making model—the analytic hierarchy process—to provide an informed decision basis for deploying embedded systems in the context of UAV-based remote sensing. One software package is combined in three software–hardware alternatives, which are profiled in hardware-in-the-loop simulations. Three objectives are used as inputs for the decision-making process. A Monte Carlo simulation provides insights into which decision-making parameters lead to which preferred alternative. Results indicate that local weights significantly influence the preference of an alternative. The approach enables relating complex parameters, leading to informed decisions about which hardware is deemed suitable for deployment in which case.

The prevalence of the invasive species African Lovegrass (Eragrostis curvula, ALG thereafter) in Australian landscapes presents significant challenges for land managers, including agricultural losses, reduced native species diversity, and heightened bushfire risks. Uncrewed aerial system (UAS) remote sensing combined with AI algorithms offer a powerful tool for accurately mapping the spatial distribution of invasive species and facilitating effective management strategies. However, segmentation of vegetations within mixed grassland ecosystems presents challenges due to spatial heterogeneity, spectral similarity, and seasonal variability. The performance of state-of-the-art artificial intelligence (AI) algorithms in detecting ALG in the Australian landscape remains unknown. This study compared the performance of four supervised AI models for segmenting ALG using multispectral (MS) imagery at four sites and developed segmentation models for two different seasonal conditions. UAS surveys were conducted at four sites in New South Wales, Australia. Two of the four sites were surveyed in two distinct seasons (flowering and vegetative), each comprised of different data collection settings. A comparative analysis was also conducted between hyperspectral (HS) and MS imagery at a single site within the flowering season. Of the five AI models developed (XGBoost, RF, SVM, CNN, and U-Net), XGBoost and the customized CNN model achieved the highest validation accuracy at 99%. The AI model testing used two approaches: quadrat-based ALG proportion prediction for mixed environments and pixel-wise classification in masked regions where ALG and other classes could be confidently differentiated. Quadrat-based ALG proportion ground truth values were compared against the prediction for the custom CNN model, resulting in 5.77% and 12.9% RMSE for the seasons, respectively, emphasizing the superiority of the custom CNN model over other AI algorithms. The comparison of the U-Net demonstrated that the developed CNN effectively captures ALG without requiring the more intricate architecture of U-Net. Masked-based testing results also showed higher F1 scores, with 91.68% for the flowering season and 90.61% for the vegetative season. Models trained on single-season data exhibited decreased performance when evaluated on data from a different season with varying collection settings. Integrating data from both seasons during training resulted in a reduction in error for out-of-season predictions, suggesting improved generalizability through multi-season data integration. Moreover, HS and MS predictions using the custom CNN model achieved similar test results with around 20% RMSE compared to the ground truth proportion, highlighting the practicality of MS imagery over HS due to operational limitations. Integrating AI with UAS for ALG segmentation shows great promise for biodiversity conservation in Australian landscapes by facilitating more effective and sustainable management strategies for controlling ALG spread.

In recent years the field of robotic navigation has increasingly harnessed semantic information in order to facilitate the planning and execution of robotic tasks. The use of semantic information focuses on employing representations more understandable by humans to accomplish tasks with robustness against environmental change, limiting memory requirements and improving scalability. Contemporary computer vision algorithms extracting semantic information have continuously improved their performance on benchmark datasets, however, most computations are expensive, limiting their use for robotic platforms constrained by size, weight and power such as unmanned aerial vehicles (UAVs), Recent advances have demonstrated the potential for navigation systems based on semantic information to be included into real-time operation of UAVs. This paper describes the development of a processing pipeline to incorporate the use of semantic information into a UAV navigation system. A navigation framework that uses the Robot Operating System (ROS) and semantic information is being developed, with simulations as a primary evaluation mechanism, preceeding deployment on hardware. The proposed system takes inputs from RGB images generated on-board the UAV and processes them in real-time to generate a semantic representation of its environment. The UAV executes subsequent actions autonomously by reasoning about the semantic content of the environment in order to accomplish a goal. Results from simulation indicated that the system is capable of extracting semantic information from camera images alone and infer plausible motion inputs for the flight controller to execute. The results also show that the system is capable of processing data in real-time and is able to enhance navigational capabilities to drive UAVs towards a higher level of autonomy.

The surge in global population growth and the escalating demand for social and economic prosperity present formidable challenges in the 21st century. However, asserting the sustainability of some ecological impact reduction initiatives, such as recycling, requires a comprehensive evaluation within various domains, including performance, ecology, and economics, and contemporary advancements in integrating quantitative assessments of material and manufacturing properties, coupled with mathematical decision-making approaches, contribute to mitigating subjectivity in determining the efficiency of recycling. This paper implements a robust multi-criteria decision-making (MCDM) approach to address the complexities of recycling, validating its implementation and effectiveness through a case study. The focus is set on the application of bio-based polylactic acid (PLA) and petroleum-based polypropylene (PP) additively manufactured (AM) parts produced through Fused Filament Fabrication (an approach to ecology/performance domains). The work introduces a cost analysis focusing on calculating thermomechanical recycling within the economic domain. The well-known Analytical Hierarchical Process (AHP) provides a structured framework for decision-making (the ecological impact domain) with the focus being on application. The assessment or recycling viability, encompassing AHP calculations, preprocessing, and supplementary tools, is provided by developing an open-source software tool for practitioners in the field of material science and manufacturing. The results indicate a preference for industrial-scaled recycling over virgin or lab-recycled manufacturing, particularly for petroleum-based polypropylene. The versatility and simple utilization of the software tool allow seamless integration for diverse use cases involving different materials and processes.

The treatment of moving objects in simultaneous localization and mapping (SLAM) is a key challenge in contemporary robotics. In this paper, we propose an extension of the EKF-SLAM algorithm that incorporates moving objects into the estimation process, which we term KISS. We have extended the robotic vision toolbox to analyze the influence of moving objects in simulations. Two linear and one nonlinear motion models are used to represent the moving objects. The observation model remains the same for all objects. The proposed model is evaluated against an implementation of the state-of-the-art formulation for moving object tracking, DATMO. We investigate increasing numbers of static landmarks and dynamic objects to demonstrate the impact on the algorithm and compare it with cases where a moving object is mistakenly integrated as a static landmark (false negative) and a static landmark as a moving object (false positive). In practice, distances to dynamic objects are important, and we propose the safety–distance–error metric to evaluate the difference between the true and estimated distances to a dynamic object. The results show that false positives have a negligible impact on map distortion and ATE with increasing static landmarks, while false negatives significantly distort maps and degrade performance metrics. Explicitly modeling dynamic objects not only performs comparably in terms of map distortion and ATE but also enables more accurate tracking of dynamic objects with a lower safety–distance–error than DATMO. We recommend that researchers model objects with uncertain motion using a simple constant position model, hence we name our contribution Keep it Static SLAMMOT. We hope this work will provide valuable data points and insights for future research into integrating moving objects into SLAM algorithms.

This research was a step towards enabling Unmanned Aerial Vehicles to use semantic information for navigation. A navigation framework was established, a method to address constraints that UAVs face during early development stages was introduced and additional steps towards more effective grid maps for semantic navigation were taken by introducing a novel mapping approach and a map representation that respects UAV flying height. This thesis supports UAV autonomy by taking steps towards higher-level navigation using meaningful representations of environments.