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Senast publicerade

Early-Age Behaviour of Post-Tensioned Low-CO2 and Fibre-Reinforced Concrete - An Experimental Study Using Distributed Optical Fibre Sensing
(2026) Bruhn, Elisabeth; Durling, Stina
The construction industry is under increasing pressure to reduce its environmental im pact, driving the development of low-carbon concrete solutions. In parallel, prestressed concrete systems provide opportunities for improved material efficiency, while the in clusion of fibre-reinforced concrete in forthcoming Eurocode provisions promotes hy brid reinforcement strategies aimed at enhancing structural performance at the service ability limit state. Utilizing distributed optical fibre sensing (DOFS) could help optimize structural designs. Thecombined use of climate-improvedconcrete, fibre reinforcement, and post-tensioning still lacks comprehensive experimental validation. This study addresses this gap through an experimental investigation of the static and long-term behaviour of post-tensioned concrete beams with different concrete compositions. Particular focus is placed on time-dependent effects such as creep, shrinkage, and relaxation, analysed using DOFS and compared with analytical predictions according to Eurocode 2. Three beam specimens were tested: a conventional reference beam, a low-CO2 con crete beam, and a fibre-reinforced low-CO2 concrete beam. All beams were cast using self-compacting concrete, where part of the Portland cement was replaced with ground granulated blast-furnace slag in the low-CO2 mixes. The results show consistent structural behaviour with expected prestress losses and de formation patterns. The low-CO2 and fibre-reinforced concretes exhibited smaller long term deformations than the conventional concrete, indicating reduced creep behaviour. In addition, the DOFS system accurately captured both the strain development and the overall structural response during tensioning and long-term monitoring. The findings indicate that reduced cement content can provide comparable or improved structural performance when time-dependent behaviour is considered. Furthermore, the analytical predictions according to Eurocode 2 tended to overestimate the long term prestress losses compared with the experimental observations, highlighting the conservative nature of the code-based approach.
A World Model Reinforcement Learning Approach for Vehicle Control: A vehicle controller based on DreamerV3 and a research platform for training and evaluating machine learning based controllers
(2026) Munck af Rosenschöld, Andreas; Wir, Oscar
The purpose of this thesis was to investigate whether a world-model-based reinforcement learning approach could be used for path following on a small off-road unmanned ground vehicle. The method involved building a 1/10 scale RC car platform equipped with a binocular camera and developing a ROS 2 graph to manage interprocess communication, including pose estimation and vehicle control. A worldmodel reinforcement learning controller was developed by adapting the DreamerV3 implementation for the path-following task. This controller was evaluated across various trajectories, including sine waves and clothoid turns on uneven grass, as well as backtracking on sand. A Pure Pursuit controller was used as a baseline. However, it was not tuned to individual paths and did not represent state-of-the-art controllers. The results indicate that the controller tracked all paths effectively and mostly smoothly, outperforming Pure Pursuit on all tasks in terms of cross-track error, successfully bridging the sim-to-real gap using a limited amount of training data. While it exhibited minor over-adjustments on certain paths and underutilisation of the steering range, the controller demonstrated an emergent behaviour of reversing to adjust its alignment at sharp corners. Consequently, this validated the successful development of the underlying research platform.
Theoretical and experimental analysis of inductor characteristics for hearing implants
(2026) Asadour, Christian; Safi, Abdulrahman
Implantable hearing devices rely on inductive coils to transfer power and signals wirelessly across the skin. The electrical and magnetic properties of these coils directly affect device effi-ciency, and size. This thesis investigates how core material, core diameter, wire diameter, and number of turns affect the inductance, DC resistance, quality factor, magnetic flux, and physical size of wire-wound inductors intended for hearing implant applications. The work was carried out in collaboration with Oticon Medical. A MATLAB model was implemented and evaluated for five core materials across seven core di-ameters and two wire diameters at a target inductance of 100 μH. Four prototype coils were also hand-wound on ferrite rod cores at Oticon Medical's laboratory and measured using a Source-tronic ST2827A precision LCR meter. The theoretical results were compared with the experi-mental measurements to evaluate how well the model reflected practical coil behavior. The findings provide a comparative framework for understanding how material and geometric choices affect coil performance and highlight key considerations for future coil design in im-plantable hearing devices.
Collaborative Robotic Arm and Humanoid Interaction for Kitting Tasks in Simulated Factory Environment: A Simulation-Based Evaluation of Motion Planning Methods for Automated Kitting in Dynamic Industrial Environments
(2026) Olsson, Marcus; Wilsborn, Elias
Future industrial automation requires robotic systems that can operate in workspaces where objects, equipment, and humans may be present. This thesis presents the development and evaluation of a simulation-based system for motion planning in a kitting task. The system was built around a gantry-mounted UR10e robot arm with a Robotiq gripper in a simulated factory environment containing a flow rack, crates, static and dynamic obstacles. The work integrated ROS 2, Isaac Sim, MoveIt 2, and several motion-planning frameworks in a containerised software architecture. Sampling-based planning with OMPL, GPU-accelerated planning with cuMotion and cuRobo, and a hybrid planner based on cuRobo MotionGen and MPC were implemented and evaluated. The planners were tested in simple motion cases, complete pick-and-place workflows, and a dynamic obstacle benchmark where the robot had to react to a newly introduced obstacle during execution. The results showed that cuRobo provided the strongest overall balance between planning speed, success rate, and motion efficiency in the static benchmark cases. cuMotion also achieved high success rates, but generally required longer planning times. The OMPL planners were computationally cheap and could be fast in successful cases, but showed lower robustness in several scenarios. The hybrid planner was able to react to dynamic changes in the environment, but its success depended on how close the obstacle appeared to the robot. When enough clearance was available, the hybrid planner recovered reliably, while recovery became less likely when the obstacle was inserted very close to the robot. The thesis shows that simulation is a useful tool for evaluating motion-planning methods for constrained industrial tasks before real-world deployment. It also shows that GPU-accelerated and hybrid planning methods are promising for robotic operation in dynamic environments, but that further work is needed before the system can fully represent realistic human-robot collaboration.
Lighthouse++ - A Search and Generative Engine Optimization Tool for VSCode
(2026) Jöeäär, Adam; Alm-Eriksson, Daniel; Hamdan, Mohammad; Johansson, Simon; Noufal, Yousef
Search engine optimization (SEO) remains an important challenge for e-commerce businesses in the ever-expanding and competitive World Wide Web (WWW) landscape. The emerging AI-driven information retrieval trends have added new dimensions to the WWW utility and further complicated the SEO problem. Traditional SEO is largely performed using external tooling, disrupting the flow as developers move back and forth between environments. In addition, current tools do not adequately consider the evolving AI-driven search landscape. This thesis presents the design and implementation of Lighthouse++, a Visual Studio Code (VS Code) extension for automated analysis and improvement of webpage quality with a primary focus on on-page SEO and Generative Engine Optimization (GEO). The system is built around Google Lighthouse and extends it with AI-assisted suggestions, a set of custom metrics, and an exploratory GEO component intended to provide heuristic feedback rather than predictive ranking-based estimates. In this regard, the thesis proposes a GEO-oriented evaluation framework based on semantic similarity and source visibility, intended to capture how webpage content may align with retrieval and answer generation in large language model systems. The developed extension audits webpages, identifies technical and structural issues, and generates suggested code changes intended to improve Lighthouse-related outcomes while also exposing GEO related feedback as an exploratory complement to traditional SEO analysis. Finally, Lighthouse++ comes with improved usability by enabling developers to code and test their websites for S&GEO in their IDEs. The project demonstrates the effectiveness of the proposed tool by evaluating and showing improved metrics across 80 pages from 18 different websites. The evaluation shows an increase in all metrics and that AI-assisted SEO and GEO are possible.