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Senast publicerade

Managing the Factory of the Future
(2026) Aronsson, Lucas; Nordgren , Ludvig
The automotive industry is undergoing a rapid transformation due to the advancements of smart technologies, changing customer behaviours, and increasing demands for sustainability, requiring manufacturing practices to be re-evaluated. Industry 5.0, the latest industrial revolution, has been developed to address this, re-emphasising the role of humans in manufacturing processes and seeking to utilise the unique strengths of humans, such as creativity and reasoning. The study aims to explore improvements to the management of manufacturing at Volvo Cars, building on the human-centric principles of Industry 5.0. The study examines the current trends in manufacturing management and the current state of management practices within the Volvo Cars Manufacturing System, ultimately proposing specific improvement suggestions for Volvo Cars manufacturing management practices. The study was conducted by a combination of literature review, on-site observations, and interviews. The study identified four key principles to achieve more human-centric manufacturing. These were the utilisation and continuous development of human-specific strengths; only involve humans when necessary or advantageous; distributing the right information to the right people; and improving working conditions, wellbeing and job satisfaction. The study also identified three currently inhibiting factors to human-centric management at Volvo Cars; time constraints, analogue systems, and human-specific strengths being underutilised. Lastly, four practical improvement suggestions were proposed, designed with the intention of being realistically possible to implement at Volvo Cars within a 5-year timespan, and to function as guidance toward a full-scale transition into Industry 5.0.
Extending a Distributed Multi-Robot Coordination Infrastructure with Predicated Broadcast and Multicast Communication
(2026) Hägglund, Tobias; Wallin, Filip
Multi-robot systems operating in distributed environments require coordination mechanisms that are both scalable and flexible. Existing approaches commonly rely on centralized controller or undiscriminating broadcast communication, both of which introduces limitations: centralization creates single points of failure and communication bottlenecks, while broadcast-based coordination results in unnecessary message processing by robots for whom a given task is irrelevant. This thesis addresses these limitations by extending an existing ROS2-based decentralized coordination infrastructure developed at Chalmers with two new predicate-based communication mechanism: predicate-based broadcast and predicate-based multicast. Predicate-based broadcast enables system-wide message distribution in which only robots whose locally stored traits satisfy a specified boolean predicate respond to and act upon a coordination request. Predicate-based multicast extends this further by facilitating communication within dynamically formed subgroups of eligible robots, selected throughout a decentralized election process. Formal semantics are provided for both these mechanisms. The implementation preserves the core design principles of the inherited infrastructure, that is, decentralized decision making, agent anonymity, and global message ordering guarantees. The mechanisms was evaluated through two simulation scenarios using RViz. The results demonstrate that selective coordination can be achieved without the need for a centralized knowledge of robot capabilities or identities. It also shows that temporary multicast groups can be formed and dissolved at runtime, and that message ordering is maintained during the lifecycle of the coordination group. The results indicate that predicatebased communication is a viable approach for achieving selective coordination in decentralized multi robot systems.
Road Section Weather and Vehicle Energy Consumption Modeling
(2026) Eriksson, Otto; Liu, Kaiyuan
Accurate vehicle energy consumption modeling for heavy-duty trucks requires precise characterization of aerodynamic drag and rolling resistance, both of which are highly sensitive to local weather and road conditions. Existing models often rely on standard atmospheric assumptions or dedicated on-board sensors, which limits their ability to capture real-world operating conditions. To address this, the present work proposes an integrated physics-based modeling framework that links road-segment level meteorological data to vehicle motion resistance without requiring additional sensing hardware. For the wind prediction model, a topographical model is used as a base and further corrected with a two-layer wind speed correction model developed to translate regional meteorological station measurements into local road-level wind estimates. The successive layers account for a global power law bias and geographic environment effects through clustering of road segments into sheltered and wind-amplified categories. Validated against over 200 repeated real-world journeys on RV (Riksväg) 40, Sweden, the model achieves a 48.4% reduction in mean absolute error over the regional baseline, consistently across seasons and travel directions, compared to using measurements from regional meteorological stations. A wind direction model in the form of a polynomial is incorporated to complete the wind characterization. For the rolling resistance component, a two-state tire thermodynamic observer is formulated, estimating tire bulk temperature from standard Tire Pressure Monitoring System (TPMS) measurements and an external temperature sensor via a Kalman filter. This enables model-based prediction of temperature-dependent rolling resistance as a function of ambient conditions and driving dynamics, without dedicated thermal sensors. The proposed framework provides a scalable, low-cost approach to weather-aware energy consumption forecasting, directly applicable to range prediction and route planning for heavy-duty trucks operating under dynamic real-world road section conditions.
Cam Phaser Simulation in GT-Suite
(2026) Sankar, Sarath
Cam phasers enable variable valve timing by changing the angular position of the camshaft relative to the crankshaft. In heavy-duty diesel engines, cam phasing can support functions such as thermal management, engine braking, and potential replacement of certain conventional engine components. Understanding the internal behaviour of a cam phaser is therefore important for further system development. However, existing supplier models provide limited access to the internal hydraulic and mechanical behaviour of the system. This thesis develops an in-house system-level model of a hydraulic Cam Torque Actuated vane phaser in GT-SUITE. The model combines the hydraulic, mechanical, and electrical domains of the system, with particular focus on the Oil Control Valve and the internal oil flow paths governing phaser operation. The model is calibrated against test-cell measurements. The results show that the model captures the actuator and cam phasing behaviour observed in the measure ments, although some deviation remain. Beyond reproducing measurable system responses, the physics-based model enables investigation of internal quantities and oil flow behaviour that cannot be directly measured in the test setup. The de veloped model therefore provides a basis for improved understanding and further development of the cam phaser system.
Enhancing Web Accessibility Remediation with LLM-based IDE Extensions
Luo, Dantong
AI coding assistants offer opportunities for web accessibility support, but evidence on how accessibility-focused guidance affects code accessibility and developer experience remains limited. This thesis presents A11yHelper, a Visual Studio Code extension integrated with GitHub Copilot Chat, and compares its accessibility quality, perceived usability, and tool preference with GitHub Copilot and CodeA11y. The evaluation combined a fixed-prompt experiment and a controlled user study with 18 participants. Both covered five tasks from two repositories. The analysis used task-specific three-level quality ratings, the SUS score, preferences between the two tools used, and descriptive categorization of written reasons. Quality differences depended on the task. A11yHelper had higher mean scores than GitHub Copilot on form labeling and keyboard navigation in both evaluations, while GitHub Copilot matched or exceeded its means on the remaining tasks. Both scored higher than CodeA11y on the task requiring information distributed across a larger file. Alternative text remained difficult, with only two of 36 user-study artifacts rated Good. Mean SUS scores were 79.38 for GitHub Copilot, 76.46 for A11yHelper, and 59.58 for CodeA11y, preference counts followed the same ordering. The study contributes an integrated accessibility assistant and descriptive evidence of its task-specific strengths and limitations.