3D reconstruction has matured into a robust technology. However, small, flexible objects such as conifer seedlings remain challenging due to their fine-scale structures, and susceptibility to movement. This study investigates and evaluates methods for reconstructing spruce (Picea abies) and pine (Pinus sylvestris) seedlings, with the aim of establishing a workflow capable of capturing geometry and texture for applications in machine learning and virtual testing environments. Two acquisition approaches were tested: photogrammetry using a RGB camera and a 3D scanner, both mounted on a robotic arm. While the scanner produced incomplete results, the photogrammetry approach successfully generated point clouds (pcl) with color information. Three different photogrammetry software were tested before relying on Agisoft Metashape and Meshroom for image processing and dense pcl generation, followed by pcl filtering in CloudCompare and meshing in Blender. Six seedlings were reconstructed to textured meshes and quantitatively evaluated using the metrics precision, recall, F1-score, mask intersection-over-union (IoU), and boundary IoU. Results showed an average mask IoU of 75.7% and F1-score of 86.1%. Pine seedlings yielded higher recall and F1-scores, whereas spruce reconstructions demonstrated higher precision. The proposed semi-automated workflow demonstrates the feasibility of reconstructing small and slender structured flexible objects, specifically conifer seedlings.

The use of physical vehicles and environments during vehicle research and development is highly resource-intensive, particularly for autonomous vehicles. Recently, digital models are therefore increasingly used instead, which require high levels of fidelity and validity. While the two aforementioned qualities are often lacking, an absence of versatility for multi-purpose use is even more prevalent in current digital models. In response to these challenges, this work presents a novel real-time multi-physics digital twin of an offroad vehicle with high levels of fidelity and validity, both regarding the vehicle dynamics and hydraulics, as well as regarding the visual representation of the environment and the exteroceptive sensor emulation. The versatility of the digital twin enables its usage for vehicle development tasks concerning mechanical components and driveline, as well as for visual machine learning tasks, such as generation of auto-annotated visual training data. Development of control algorithms leveraging both visual input and mechanical systems is also enabled. Furthermore, the real-time capability allows for Hardware-in-the-Loop and Vehicle-in-the-Loop simulation. The modeling, calibration, and real-world validation of the digital twin is presented, with an emphasis on the vehicle dynamics and hydraulics. The shown validity enables advancements in the development of autonomous offroad vehicles.

The dataset contains raw RGB image data and finalized textured 3D meshes of six conifer seedlings: three Norway spruce (Picea abies) and three Scots pine (Pinus sylvestris). Each seedling mesh were reconstructed using the corresponding 288 RGB images.

The raw image data can be used for photogrammetric reconstruction workflows, while the provided .glb files contain textured meshes suitable for import into virtual environments and 3D software such as Blender and Unity.

Copper is a strategic raw material and an important component in electric motors, widely used across industries because of its excellent conductivity and recyclability. It plays an important role in the transformation from fossil fuel-based systems to green, electrified systems. However, substantial material losses continue throughout the lifecycle of electric motors, even with copper’s intrinsic capacity for circularity. Also, copper’s increasing demand, which is driven by the emergence of electric vehicles, industrial electrification, and renewable energy infrastructure, poses questions regarding its sustainable supply. The recovery of secondary copper sources from end-of-life (EoL) products is becoming more and more important in this context. However, it is still difficult to achieve circularity of copper, especially from industrial electric motors. This study investigates the challenges of closing the loop for copper during the lifecycle of motors in industrial applications. Based on an examination of EoL strategies, material flow insights, and practical investigation, the research pinpoints significant inefficiencies in the current processes. The widespread use of scraping as an approach of end-of-life management is one significant issue. Most of the electric motors are not built to separate their components, which makes both mechanical and manual disassembly difficult. The quality of recovered copper is thus compromised by the dominance of mixed metal shredding methods in the recycling step. This study highlights the need for systemic changes in addition to technical solutions to address copper circularity issues. It requires a focus on circularity in designing, giving disassembly and metal recovery a priority. This study focuses on circularity and its technological challenges in a value chain of copper. It not only identifies different processes such as supply chain disconnections and design constraints, but it also suggests workable solutions to close the copper flow loop in the electric motor sector. Copper quality and recovery is ultimately a problem involving design, technology, and cooperation, in addition to resources. This study supports the transition to a more sustainable and circular electric motor industry by offering a basis for directing such changes in industry practices and prospective EU regulations.

Object detectors for autonomous forestry operations have previously been developed mainly by training on physical manually annotated data, which is both time-consuming and costly. Since the ground truth in the virtual model is known, the training data can be auto-annotated, enabling the creation of larger training datasets, while also improving time and cost efficiency. In this work, a virtual environment in Unity is used in co-simulation with a real-time digital twin of a physical forestry vehicle, to generate realistic auto-annotated training data, as captured by an onboard stereo camera. First, it is shown that a log detector trained on physical data can detect logs in the virtual environment. Second, new detectors are trained, using different shares of virtual and physical data. It is shown that a detector trained using only virtual data, can learn to detect logs in the physical world. Moreover, virtual pre-training is shown to improve the performance of physically trained and tested detectors, both at low availability of physical training data, and in terms of domain generalization. A detailed detector performance analysis also highlights further potential and opportunities for future improvements. Furthermore, the real-time capable virtual models enable future machine learning tasks utilizing different levels of Hardware-in-the-Loop. 

This study presents a metaheuristic approach to coverage path planning for ground-based forest operations, focusing on minimizing path lengths for forest vehicles while considering terrain characteristics and vehicle parameters. Forest vehicles can usually tolerate higher pitch than roll angles, which makes them vulnerable to rollover. To mitigate that, this method utilizes a genetic algorithm to optimize the sequence of nodes, which are scattered over the site with equal spacing. The coverage path planner then calculates the Dubins path distance between every node in the fitness function, together with penalties for exceeding pitch, roll and soil moisture constraints for the vehicle. This ensures that the path planner tries to make the most traversable path as possible, while trying to minimize the driving distance. Two synthetic test sites resembling primitive challenging terrains, and one real site were utilized to theoretically evaluate the proposed method. The results show that aligning the node patterns with the critical slope headings, instead of having a straight pattern, had little effect on the path length. However, square grids can yield shorter paths across multiple runs, while triangular grids ensure consistent results in single runs. A two-hectare site took 43 minutes to calculate on average. This suggests that further development of the path planner could lead to significant improvements, enabling the management of sites larger than a few hundred nodes. However, the calculation time is justified for the reduced path length during deployment. The study presents a methodology that supports manual operators and establishes foundations for full autonomy.

Sustainable forestry requires efficient regeneration methods to ensure that new forests are established quickly. In Sweden, 99% of the planting is manual, but finding labor for this arduous work is difficult. An autonomous scarifying and planting machine with high precision, low environmental impact, and a good work environment would meet the needs of the forest industry. For two years, a collaborative group of researchers, manufacturers, and users (forest companies) has worked together on developing and testing a new concept for autonomous forest regeneration (Autoplant). The concept comprises several subsystems, i.e., regeneration and route planning, autonomous driving (path planning), new technology for forest regeneration with minimal environmental impact, automatic plant management, crane motion planning, detection of planting spots, and follow-up. The subsystems were tested separately and integrated together during a field test at a clearcut. The concept shows great potential, especially from an environmental perspective, with significantly reduced soil disturbances, from approximately 50% (the area proportion of the area disturbed by disc trenching) to less than 3%. The Autoplant project highlights the challenges and opportunities related to future development, e.g., the relation between machine cost and operating speed, sensor robustness in response to vibrations and weather, and precision in detecting the size and type of obstacles during autonomous driving and planting.