About the project

Objective

The purpose of the project is to enable an end-user to make practical and useful conclusions about water content in a high-voltage transformers from indirect measurements provided by sensor data, including an understanding of the water content in the paper insulation and moisture in oil. To do so an advanced digital twin model will be developed that includes the simulation of non-uniform water distribution and allows for interpreting measurements from sensors. This will enable accurate life-cycle assessment.

Two workers in safety gear walk towards large electrical equipment at an outdoor power substation, surrounded by metal structures, under a cloudy sky. Snow covers the ground and equipment.

Background

Electric insulation is an important part of stable and safe operation of high-voltage components and plays a key role in power delivery and meeting future rising electricity demand. One important component of this is the moisture dynamics in the transformer insulation. Moisture affects aging, reliability, and can lead to dielectric breakdown and subsequent component failure.  Hence, it plays a crucial role in power generation and distribution. Insight into how water residual is distributed inside the transformer insulation is crucial for evaluating its safety, aging and overall reliability.

This project uses mathematical insight to build a digital twin that reliably and continually updates the moisture model based on real-time data.  This will allow insight into information about various processes related to the moisture dynamics that are challenging to obtain otherwise and lays the foundation for real-time monitoring systems and accurate life-cycle assessment of key assets, which contributes to building a smart society.

Crossdisciplinary collaboration

The project is a collaboration between KTH, Hitachi and Imperial College.

Principal Investigators (PIs)

About the project

Objective
Modern autonomous systems are required to operate in close collaboration with human operators in complex, dynamic environments, such as industrial settings, maritime operations, and disaster-response scenarios. In these contexts, autonomy must remain safe, adaptive, and responsive to human inputs, environmental disturbances, and evolving task specifications in real time.

The project aims to develop a unified control framework that enables autonomous systems to safely and adaptively collaborate with humans in complex and unstructured environments. Specifically, it will bridge high-level task allocation and trajectory planning with low-level feedback control using the mathematical paradigm of hybrid systems, and it will enable interactive decision-making and human supervisory inputs through the integration of game theoretic principles.

The project will further translate this theoretical framework into computationally tractable controllers suitable for real-time implementation on mobile autonomous systems. The proposed approach will be validated through real-world experiments in maritime robotics scenarios, including diver–robot collaboration missions in both controlled test tanks and open-water environments. By integrating new theory and experimental deployment, the project seeks to enable trustworthy, resilient, and adaptive human-in-the-loop autonomy across multiple time scales, from high-level decision-making to low-level feedback control.

A modern, two-storey building with a curved glass and metal upper level and metal lower level. Two people stand on the upper deck, and an outdoor staircase leads to the roof. A brick building is visible in the background.
The KTH Marinarium experimental facility, a controlled underwater test tank equipped with motion capture systems, will be used for experimental validation.
A cylindrical underwater robot is suspended by a cable in a blue-lit tank, surrounded by cameras and sensors attached to the tank walls, with light rays streaming through the water.
An autonomous underwater vehicle during docking experiments, illustrating real-time deployment of advanced control methodologies on mobile autonomous systems.
Four people prepare equipment on a small boat moored by a stone quay. Two wear SMARC jackets, and one wears a red life jacket. The water is calm and industrial buildings are visible in the background.
A field deployment at Beckholmen’s Dry Dock, a semi-controlled open-water environment enabling validation under realistic environmental conditions.
A 3D digital rendering of a shipyard and harbour area, showing docks, cranes, ships, and various buildings near the water, with a cityscape and a large cruise ship visible across the river.
Digital twin of the dry dock environment in the SMaRC simulator, supporting the design and testing of human-in-the-loop autonomy.

Background
In recent years, autonomous systems have been increasingly deployed to work alongside human operators in executing complex, coordinated tasks. Prominent examples include warehouse automation, assembly-line operations, and intelligent logistics, where systems must handle tasks such as sorting, inspection, or replenishment under strict spatial constraints (e.g., limited workspaces and predefined pathways) and temporal requirements (e.g., synchronization and time-critical deliveries).

These challenges become even more pronounced in extreme environments, including underwater robotics, space exploration, and disaster-response scenarios, where systems operate in unstructured and uncertain conditions characterized by limited communication, poor visibility, and strong environmental disturbances. Across both industrial and extreme domains, the ability to adapt task allocation, planning, and control in real time is essential to ensure efficiency, resilience, and safety in human–robot collaboration.

A person holds a small cardboard box being carried by a drone with orange lights, outdoors at sunset, suggesting drone delivery service.
A scuba diver underwater, wearing full diving kit, operates a blue underwater drone or remotely operated vehicle near a vertical structure.

Autonomous systems are increasingly employed alongside humans to perform complex tasks in both structured and unstructured environments.

The state-of-the-art control architecture for autonomous systems typically follows a layered structure consisting of task allocation, trajectory planning, and real-time low-level feedback control. Task allocation determines how tasks are distributed among agents, planning generates trajectories that satisfy spatial and temporal specifications, and low-level control ensures accurate tracking and safety. While effective in structured settings, this hierarchical stack is not inherently designed to account for real-time human intervention, changing objectives, or unexpected obstacles, which are central features of human-in-the-loop scenarios.

Cross-disciplinary collaboration
The project brings together complementary expertise in applied mathematics (hybrid systems, formal methods, and game theory), control engineering (model predictive control), and maritime and underwater robotics to address the challenges of human-in-the-loop autonomous systems in extreme environments. This cross-disciplinary collaboration tightly integrates mathematical modelling, control design, and real-world robotic experimentation.

PIs: Giuseppe Belgioioso, Dimos Dimarogonas, and Ivan Stenius.

About the project

Objective
This project aims to provide mitigation solutions for decision-makers to reduce human and environmental exposure to particle emissions caused by the transport sector. Through the use of cross-disciplinary approaches, the project develops methodologies that are tested and validated by adopting Stockholm as a digital sandbox.

Background
In our daily city life, we are constantly exposed to means of transport such as passenger vehicles, heavy-duty vehicles, and rail transportation. These vehicles release toxic particle emissions originating from exhaust and non-exhaust sources. Recent projections indicate an increase of non-exhaust emissions in urban areas from 0.5% in 2021 to 67% in 2050. Non-exhaust emissions are the primary source of inhalable Particulate Matter (PM). With a diameter smaller than 10 µm, PM10 can be inhalable by humans causing inflammations and other health diseases. On the other hand, PM with a size larger than PM10 can deposit over the nearby infrastructures contributing to environmental pollution. 

Stockholm is among the forefront European cities capable of monitoring in real-time level of PM and policy makers strive to mitigate these emissions. However, despite these efforts, the concerns regarding the increase of non-exhaust PM in the urban areas remain critical. 

About the Digital Futures Postdoc Fellow
Henri Giudici completed his Ph.D. in Civil and Environmental Engineering at the Norwegian University of Science and Technology – NTNU (Norway). He specialized in vehicle tire-pavement interactions in winter conditions. His research supported the Norwegian road authority in reducing road salt application rates during winter. Between 2019 and 2022, he served as an industrial Principal Scientist, developing technologies and data-driven approaches for assessing the quality of transport infrastructures. In 2022, he continued his academic career as a researcher in systems engineering at the University of South-Eastern Norway – USN (Norway). In his current Digital Futures postdoc, Henri fosters agile approaches to integrate scientific evidence into policy-making by bridging transport tribology, systems engineering and data science.

Main supervisor
Ellen Bergseth, Associate Professor at Department of Engineering Design, KTH.

Co-supervisor
Ulf Olofsson, Professor at Department of Engineering Design, KTH.

About the project

Objective
To develop DeepAqua-II, a robust, scalable deep-learning system for global surface-water monitoring using SAR time-series data.

Specific objectives (O1–O5):

  1. O1: Design a technique to normalize SAR pixel intensity values so models remain resilient to sensor adjustments.
  2. O2: Build a self-supervised semantic segmentation model using SAR time-series without needing optical data.
  3. O3: Add support for L-band SAR sensors to detect water under vegetation.
  4. O4: Quantify changes in surface-water extent across multiple climate regions for 2015–2027.
  5. O5: Communicate and disseminate results to maximize impact, including training and capacity building.
A satellite orbits Earth, capturing data on surface water changes. Text reads, DeepAqua-II: Scaling the quantification of surface water changes to global levels with deep learning.

Background
Surface water is declining worldwide, requiring more accurate monitoring.

Crossdisciplinary collaboration

Hydrology & Environmental Sciences

Computer Science & Machine Learning

Nature of collaboration

About the project

Objective

This project is primarily situated at the intersection of Rich and Healthy Life and Cooperate within the Digital Futures research matrix. It explores how AI-enhanced XR environments can foster meaningful human-AI collaboration across immersive scenarios. The project also contributes to the societal context by designing XR experiences that support personal development, creativity, and skill-building. Through dynamic, interactive, and adaptive environments, the system enables users to engage in: (1) self-paced learning and creative prototyping, (2) scenario rehearsal and training, and (3) immersive co-creation. Central to the Cooperate theme is the exploration of multi-agent and multi-user interaction, focusing on how AI agents can support, guide, or adapt in real time to facilitate shared decision-making and effective human-human collaboration, mediated or enhanced by AI.

Background

Artificial Intelligence (AI) and Extended Reality (XR), which includes Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), represent two rapidly evolving domains that are reshaping the modalities through which humans engage with digital systems. AI technologies are now deeply embedded in a wide array of applications, ranging from algorithmic recommendation systems and automated content generation to complex decision-support tools used in domains such as traffic management and surgical procedures. Concurrently, XR technologies are becoming increasingly mainstream due to advances in hardware and the availability of affordable head-mounted displays, making immersive experiences more accessible to both industry and the general public. Although both AI and XR are subjects of extensive research, the intersection of these fields, particularly within the context of Human-Computer Interaction (HCI), remains relatively under-investigated. In particular, the integration of AI-driven agents, including conversational agents, within XR environments poses novel questions regarding interaction design, user experience, and the role of intelligent systems as co-actors in immersive settings.

Cross-disciplinary collaboration
The researchers in the team represent the KTH School of Electrical Engineering and Computer Science and RISE Research Institutes of Sweden, Digital Systems Division.

About the project

Objective
This project aims to explore the emerging gigantic multiple-input multiple-output (gMIMO) technology in the physical layer, using methodology from signal processing and information/communication theory. gMIMO relies on using 10 times more antennas in base stations and user devices than in current networks, by operating in the upper mid-band where antennas are smaller. This enables massive spatial multiplexing and beamforming. The research results are anticipated to be applied to 6G and future wireless communication networks and provide valuable insights into their integration within a wide range of digital transformation.

Background
Sixth-generation (6G) wireless communication is crucial for enabling society’s digital transformation and future cyber-physical world. Many transformative applications, such as ultra-massive connectivity, immersive extended reality (XR) communication, smart factories, and intelligent transportation systems, can be efficiently facilitated by intelligent 6G wireless networks. To support these extensive usage scenarios, delivering exceptionally high data rates to many users is important. Since fourth-generation (4G) wireless networks, multiple-input multiple-output (MIMO) technology has been a key technology to deliver higher rates.

With the advancement of wireless networks, MIMO technology has also significantly evolved with the continually increasing number of antennas. To further empower the 6G and future wireless networks, gigantic MIMO (gMIMO) is highly anticipated by utilizing hundreds or a thousand antennas, which is the major focus of this project.

About the Digital Futures Postdoc Fellow
Zhe Wang received his PhD degree in 2025 from the School of Electronic and Information Engineering at Beijing Jiaotong University, China. His research focuses on promising 6G wireless communication technologies, including gigantic MIMO, cell-free massive MIMO, and near-field communication.

Main supervisor
Emil Björnson, KTH

Co-supervisor
Vitaly Petrov, KTH

About the project

Objective
The project aims to enhance the resilience and preparedness of Stockholm against multiple hydrometeorological hazards, primarily flooding, droughts, and heatwaves, by developing innovative monitoring systems, assessing systemic risks, and co-creating adaptive warning systems and action roadmaps with stakeholders.

Background
Urban areas are increasingly vulnerable to hydrometeorological hazards, which are growing in frequency and severity due to climate change and urbanization. These events pose significant risks to Critical Societal Services and Infrastructure (CSSIs), including transport, water, and energy systems. Traditional risk analyses often focus on single hazards and overlook cascading impacts across interconnected systems. CASCADE addresses this gap by integrating advanced AI-based prediction models, network analysis, and stakeholder-driven approaches to build a comprehensive framework for disaster preparedness and climate adaptation.

Cross-disciplinary collaboration
CASCADE brings together expertise from KTH Royal Institute of Technology, the City of Stockholm, and RISE. The collaboration integrates environmental and water engineering, computer science, systems analysis, and urban planning with practical municipal and industry perspectives. This transdisciplinary partnership ensures that solutions are scientifically rigorous, technologically advanced, and practically applicable.