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This article introduces a novel collaborative optimal motion planning framework for parallel aerial 3D printing. The proposed novel framework is efficiently capable of handling conflicts between the utilized Unmanned Aerial Vehicles (UAVs), as they follow predefined paths, allowing for a seamless enhancement of aerial 3D printing capabilities by employing multiple UAVs to collaborate in a parallel printing process. The established approach ingeniously formulates UAVs’ motion planning as a multi-constraint optimization problem, ensuring minimal adjustments to their velocities within specified limits. This guarantees smooth and uninterrupted printing while preventing collisions and adhering to the requirements of aerial printing. To substantiate the effectiveness of our proposed motion planning algorithm, an extensive array of simulation studies have been undertaken, encompassing scenarios where multiple UAVs engage in the fabrication of diverse construction shapes. The overall novel concept is being extensively validated in simulations, while the obtained results promise for enhancing the viability and advancing the landscape of aerial additive manufacturing. Validerad;2024;Nivå 2;2024-01-22 (signyg)Full text license: CC BY-4.0

In this article, we present an edge-based architecture for enhancing the autonomous capabilities of resource-constrained aerial robots by enabling a remote nonlinear model predictive control scheme, which can be computationally heavy to run on the aerial robots' onboard processors. The nonlinear model predictive control is used to control the trajectory of an unmanned aerial vehicle while detecting, and preventing potential collisions. The proposed edge architecture enables trajectory recalculation for resource-constrained unmanned aerial vehicles in relatively real-time, which will allow them to have fully autonomous behaviors. The architecture is implemented with a remote Kubernetes cluster on the edge side, and it is evaluated on an unmanned aerial vehicle as our controllable robot, while the robotic operating system is used for managing the source codes, and overall communication. With the utilization of edge computing and the architecture presented in this work, we can overcome computational limitations, that resource-constrained robots have, and provide or improve features that are essential for autonomous missions. At the same time, we can minimize the relative travel time delays for time-critical missions over the edge, in comparison to the cloud. We investigate the validity of this hypothesis by evaluating the system's behavior through a series of experiments by utilizing either the unmanned aerial vehicle or the edge resources for the collision avoidance mission. Full text license: CC BY

There are many sources that farmers utilize to determine fertilizer needs for crops such as private and public labs, crop advisors, and fertilizer dealers. In many cases, these sources provide recommendations for a specific crop that can vary greatly, which can lead to large differences in cost. An experiment was established in 2021 with 12 sites across the state of Utah in alfalfa, small grains, and corn to test and compare fertilizer recommendations from five labs. The recommendations tested were from two public labs (Utah State University and the University of Idaho) and three commercial labs located in the Western United States. A composite soil sample was sent to multiple labs for analysis and the corresponding macronutrient and micronutrient rates recommended by each lab were applied at each site. Yield and forage quality data were collected from sites from 2021-2023 to evaluate treatment impacts. Fertilizer treatments had little to no impact at silage corn or alfalfa sites, but differences in yield and forage quality were observed at small grain forage sites. High variability in reported soil test results for the same composite soil samples was observed from three commercial soil testing labs. Differences in soil test results are sometimes due to the accuracy of each lab's analyses, but they are also influenced by different chemical procedures being used to determine nutrient levels. Fertilizer recommendations from the five laboratories varied greatly, both for types of nutrients and rates being recommended. This is likely due to a combination of differences in soil test values (minor influence) and the fertilizer recommendation philosophies (major influence) utilized by each lab. When the recommendations were applied in field trials, higher application rates often resulted in increases in soil nutrient concentrations, but the ratio of the application rate to changes in nutrient levels varied greatly among sites and treatments. Applying higher rates to increase soil nutrient levels doesn't work for all nutrients and situations and is often not economical. The results of this study demonstrate that growers should use caution when selecting fertilizer recommendations and that there is opportunity for greater public-private coordination of fertilizer recommendations.

This thesis presents an investigation on innovative approaches to agricultural management, addressing challenges in both viticulture and turfgrass management. The first topic of this thesis introduces the Adaptive Crop Load Estimation (ACLE) method, a deep learning-based grape counting approach designed to alleviate the need for extensive annotated datasets. By training the model on a limited set of images, this method demonstrates promising results in accurately estimating grape cluster counts across different zones in the vineyards, with an average Mean Absolute Error (MAE)/Root Mean Square Error (RMSE) of 0.86/0.66. The ACLE method aims to reduce the cost of deploying automated grape counting systems by minimizing manual image annotation efforts and enabling model reusability across different vineyards. The second topic of this thesis delves into the realm of Turfgrass management, recognizing its pivotal roles in environmental health and aesthetics. Focusing on the challenges posed by spot- based diseases, the study introduces the Spot Treatment Pathfinding and Scheduling (STPAS) method. This framework employs Unmanned Ground Vehicles (UGV) for targeted spot spraying, optimizing robot stops and trajectories based on varying scenarios such as different spot sizes and robot capabilities. The trajectory planner developed within STPAS utilizes GPS coordinates and the radius of affected areas to determine efficient stops and paths for autonomous vehicles. Comparative analysis on the developed simulators reveals that STPAS reduces the distance traveled and time taken for spot spraying by over 50% compared to conventional boom-based sprayers, thereby enhancing both economic and environmental sustainability in Turfgrass management practices. This thesis explores solutions for improving agricultural practices, specifically focusing on grapevine cultivation and turfgrass management. The first part introduces a novel method called Adaptive Crop Load Estimation (ACLE), which employs deep learning to accurately count grape clusters in vineyards. Unlike traditional methods requiring extensive annotated data, ACLE demonstrates significant results with minimal training images, aiming to reduce the cost of automated grape counting systems and enhance their adaptability across various vineyards. In the second part, the thesis delves into development of planning algorithm for precision spot spraying. Addressing challenges posed by spot-based diseases, the study introduces the Spot Treatment Pathfinding and Scheduling (STPAS) method. This algorithm provides robot stops and optimizes routes based on different scenarios such as spot sizes and robot capabilities. Comparative analysis of the simulation results reveals that STPAS improves efficiency, reducing both the distance traveled and time taken for spot spraying compared to boom-based sprayers. This not only benefits economic considerations but also contributes to environmental sustainability in turfgrass management practices. Master of Science

The iron and steel industry is a major emitter of carbon dioxide globally. To reduce their carbon footprint, the iron and steel industry pursue different decarbonization strategies, including deploying bio-based materials and energy carriers for reduction, carburisation and/or energy purposes along their value-chains. In this study two potential roles for biomass were analysed: (a) substituting for fossil fuels in iron-ore pellets induration and (b) carburisation of DRI (direct reduced iron) produced via fully hydrogen-based reduction. The purpose of the study was to analyse the regional demand-driven price and allocative effects of biomass assortments under different biomass demand scenarios for the Swedish iron and steel industry. Economic modelling was used in combination with spatial biomass supply assessments to predict the changes on relevant biomass markets. The results showed that the estimated demand increases for forest biomass will have significant regional price effects. Depending on scenario, the biomass demand will increase up to 25 percent, causing regional prices to more than doubling. In general, the magnitude of the price effects was driven by the volumes and types of biomasses needed in the different scenarios, with larger price effects for harvesting residues and industrial by-products compared to those of roundwood. A small price effect of roundwood means that the incentives for forest-owners to increase their harvests, and thus also the availability of harvest residues, are small. Flexibility in the feedstock sourcing (both regarding quality and geographic origin) will thus be important if forest biomass is to satisfy demands in iron and steel industry. Full text license: CC BY 4.0;

Localization algorithms that rely on 3D LiDAR scanners often encounter temporary failures due to various factors, such as sensor faults, dust particles, or drifting. These failures can result in a misalignment between the robot’s estimated pose and its actual position in the global map. To address this issue, the process of global re-localization becomes essential, as it involves accurately estimating the robot’s current pose within the given map. In this article, we propose a novel global re-localization framework that addresses the limitations of current algorithms heavily reliant on scan matching and direct point cloud feature extraction. Unlike most methods, our framework eliminates the need for an initial guess and provides multiple top-� candidates for selection, enhancing robustness and flexibility. Furthermore, we introduce an event-based re-localization trigger module, enabling autonomous robotic missions. Focusing on subterranean environments with low features, we leverage range image descriptors derived from 3D LiDAR scans to preserve depth information. Our approach enhances a state-of-the-art data-driven descriptor extraction framework for place recognition and orientation regression by incorporating a junction detection module that utilizes the descriptors for classification purposes. The effectiveness of the proposed approach was evaluated across three distinct real-life subterranean environments. Validerad;2023;Nivå 2;2023-09-22 (joosat);CC BY 4.0 License

The effect of ecologisation on crop production performance in Sweden, measured as technical efficiency, was investigated by incorporating ecologisation into the production function and technical inefficiency determinant model. An unbalanced panel of data from the Swedish Farm Accounting Data Network (FADN), comprising 1944 observations on 346 crop farms 2009–2019, was used in stochastic frontier analysis. Ecologisation indicators considered were crop diversity index, organic farming, and environmental subsidies. The results showed that ecologisation affected the technical efficiency of crop production. Crop diversity index for the current year and the two preceding years had a positive effect on performance, with crop diversity index for current year in particular being positively associated with production performance of crop production. Organic farming was estimated to be negatively significantly associated with production performance. Rural subsidies were positively associated with production performance, but no association was found between environmental subsidies and production performance. Mean technical efficiency was 0.715, with higher values in southern Sweden than northern Sweden. These findings on the impact of ecologisation on arable farming can be useful when designing policies to encourage farmers to adopt ecologisation approaches.

Editorial on the Research TopicEnhanced human modeling in robotics for socially-aware place navigation

Livestock risk protection (LRP) insurance is a partially subsidized livestock insurance provided by the United States Department of Agriculture (USDA) Risk Management Agency (RMA) that provides protection to producers against unexpected price declines. This fact sheet presents specific coverage information for swine and presents optimal coverage options based on historical contract performance.