Science Track (Rutger Hauerzaal)

The role of geospatial data and precise positioning in the development of digitalisation and robotics has grown drastically. This evolution challenges the public sector to adapt their spatial data strategy to ensure accuracy, timeliness and sharing of geospatial data and positioning to meet the new needs arising from developments such as intelligent traffic, drones, digitalisation of forestry and agriculture, global trading and finance and other location-based services. Additionally, digital twins, that model real-world processes, such as climate or oceanic digital twins, need real-time accurate geospatial data. As part of these new applications, it is vital to understand how we can share geospatial data efficiently for the needs of real time decision making.

The internal reasoning of intelligent cyber-physical systems gets more complex and opaque with their increasing autonomy. This creates challenges for ensuring safety and correctness during development, diagnosis, and interaction. Collaboration between humans and machines is particularly prone to misunderstandings and false expectations, leading to poor results and even dangerous accidents. Extra effort is needed to regain transparency through explanations of internal behaviour. However, not all explanations are appropriate or useful. They must be tailored to a certain purpose, their recipient, and their situational context. Also, explanations must address the overall system behaviour, not only internal AI algorithms. This talk gives an overview of requirements for useful explanations, shows how tailored explanations can be generated autonomously, and presents challenging research questions.

Whether you are watching a launch site catching a starship booster on reentry or sitting in a self-driving car that is changing lanes, the technology behind these actions is automatic control. Control is a discipline at the intersection of engineering, math and physics that provides the tools to make machines behave the way you want. 

At the core of a successful automatic control design is a good physical-mathematical description of the system you want to control. This description is usually hard to get, and even if you have it, it is unlikely that you will be able to immediately design a controller for it. 

This talk will highlight recent efforts by the control systems community to automate control design using data. It will describe the fundamental principles behind an end-to-end process that, after collecting experimental data from the system to be controlled, encodes the data into an optimization program whose solution yields a provably correct control algorithm. 

The talk will discuss what is left to be done and how these results fit in a world where data and machine learning are taking over. 

Generative AI (GenAI) has been praised to automate and accelerate engineering processes even beyond code and test generation. The role of today’s software engineers even for embedded systems is being redefined and expertise on system architecture, orchestration, and large-scale refactoring is defining daily activities especially in the context of agentic software engineering. In this talk, the speaker is sharing selected highlights and critical reflections on the benefits and challenges of GenAI from several research projects in the area of automotive software engineering. A particular light is also shed on insights from and reflections about the Swedish research funding landscape in the context of AI, autonomous systems, and software. 

The design of bionic limbs is undergoing a fundamental shift, moving beyond isolated technical performance toward truly human-centered, intelligent systems that function seamlessly in the real world. This talk addresses the challenge of achieving embodied intelligence in bionic limbs, with an emphasis on lower-limb prostheses operating in complex, dynamic environments alongside their users. It highlights recent advances in compliant mechatronic design, human-in-the-loop optimization, and AI-based control architectures that tightly couple physical embodiment, sensing, and decision-making.

These developments have the potential to enable prosthetic systems that adapt in real time, respond naturally to human intent, and reduce the cognitive burden on the user. By embedding intelligence directly into both the mechanical and control layers, bionic limbs can become more intuitive, robust, and responsive. These efforts aim to support the development of a new generation of prosthetic technologies that can enhance mobility, independence, and quality of life.

Linear integrators are well known for their ability to counter static forces and improve low-frequency disturbance rejection properties in control systems. However, linear integrators introduce phase lag, which is a frequency-dependent time shift or delay. Since the early introduction of the Clegg integrator, nonlinear integrators have held the promise of providing phase advantages over linear integrators. For five currently known nonlinear integrators, an overview is provided of recent developments in stage motion control. Benchmark examples are taken from the industrial practice of wafer scanners, which form the pivotal machines used in the manufacturing of computer chips.