About the project

Objective
Wildfire monitoring involves two main problems, i.e., active fire detection and burnt area mapping. Active fire detection aims to find the ongoing wildfire hotspots, while burnt area mapping is expected to detect the areas affected by wildfire.

This project mainly focuses on large-scale wildfire burnt area mapping and near real-time wildfire monitoring. In view of the limited transfering performance of the existing wildfire monitoring algorithms on a larger scale and various climate zones, this project aims to develop large-scale or even globally applicable models by exploiting global coverage, multi-source satellite remote sensing data and advanced machine learning/deep learning techniques.

Background
Wildfire has coexisted with human societies for more than 350 million years, always playing an important role in shaping the Earth’s surface and climate. Across the globe, wildfires are becoming larger, more frequent, longer-duration, and tend to be more destructive in terms of lives lost and economic costs because of climate change and human activities.

To reduce the damages from such destructive wildfires, it is critical to track wildfire progressions in near real-time, or even in real-time, to support fire-fighting and keep everything under control. Satellite remote sensing enables cost-effective, accurate, and timely monitoring of the wildfire progressions over vast geographic areas. The free availability of global coverage Landsat-8, and Sentinel-1/2 satellite data opens a new era for global land surface monitoring, providing an opportunity to analyse wildfire impacts around the globe.

About the Digital Futures Postdoc Fellow
Puzhao Zhang is a research fellow at the Division of Geoinformatics, Royal Institute of Technology. He received his PhD in Pattern Recognition and Intelligent Systems from Xidian University, China, at the end of 2019. His PhD research was focused on remote sensing and deep learning for change detection.

Since fall of 2017, he has worked on wildfire monitoring with radar and optical remote sensing and deep learning as a joint PhD student at KTH. His research interests include satellite imagery analysis, change detection, machine learning, deep learning, and spatio-temporal modelling for monitoring environmental change and biomass carbon dynamics.

Main supervisor
Yifang Ban, Professor, Division of Geoinformatics, KTH.

Co-supervisor
Josephine Sullivan, Associate professor, Division of Robotics, Perception and Learning, KTH.

Watch the recorded presentation at Digitalize in Stockholm 2022 event.

About the project

Objective
Our research project aims to propose a complete, fully distributed scheme in intelligent task allocation and security-based controller synthesis with relative displacement or bearing-only information for networked multi-agent systems like UAVs or UGVs. The designed methods are expected to be used directly in multiple unmanned autonomous systems. The research plays a significant role in improving the autonomy and swarming of the pursuit system and has great application potential in search and rescue, intelligent transportation, and public protection.

Background
Multiple autonomous unmanned systems, such as unmanned aerial vehicles, unmanned underwater vehicles, and unmanned ground vehicles, are projected to play important roles in many industrial and societal applications, such as search and rescue, cooperative payload carrying, and warehouse management. In the community of multi-agent control, the pursuit-evasion game has been a hot topic whose objectives are to achieve optimal sensor placement intelligent and distributed task allocation, following which the control problem is formulated as a distributed optimization problem with a defined performance index. The objectives of this project thus include the exploration of cooperative and distributed optimal control strategies with relative position measurements or bearing-only information.

About the Digital Futures Postdoc Fellow
Panpan Zhou is a postdoctoral researcher at the Department of Mathematics at KTH Royal Institute of Technology. Before joining KTH, she was a Postdoctoral Fellow in the Department of Mechanical and Automotive Engineering at the Chinese University of Hong Kong. She received her Bachelor’s degree in the School of Automation from Northwestern Polytechnical University, Xi’an, China, in 2017 and her PhD from CUHK in 2021. Her research interests include control theory and applications of multi-agent systems and motion planning of micro aerial vehicles.

Main supervisor
Xiaoming Hu, professor, Department of Mathematics, KTH.

Co-supervisor
Bo Wahlberg, professor, Division of Decision and Control Systems, KTH.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event.

About the project

Objective
The objective of this project is to learn distributed control policies for multi-robot systems that scale on-demand, in multi-laterally evolving complex dynamic environments.

Background
Multi-robot systems such as drone swarms offer unparalleled advantages in assisting ground-based human-robot teams in humanitarian, and disaster-response missions thanks to their ability to operate in remote, communication-denied environments. However, in practical deployments such systems must learn how to balance various auxiliary objectives on-demand for maximizing the collective utility in highly dynamic environments. For example, a robust drone swarm control policy must prioritize the most severely affected area in a disaster-response event, and direct more robots to tackle it, on-the-fly.

In this project, we aim to design a framework that can help us learn such control policies while taking into account the robot’s varying capabilities. Upon designing such a framework, we further understand that we can minimize the human bias in hand-engineered task prioritizing.

About the Digital Futures Postdoc Fellow
Malintha Fernando received his Ph.D in 2023 from Indiana University, Bloomington (USA). His doctoral research focused on designing cooperative scalable control policies for multi-drone systems that are robust to communication failures. Such policies lend themselves to many Urban Air Mobility (UAM) applications such as autonomous parcel delivery, where decentralized decision-making under local information is required to achieve the necessary scalability over the geographical span and in the number of vehicles.

Prior to joining KTH, Malintha worked as a visiting lecturer in Machine Learning at Indiana University. He has completed an internship at Open Robotics, Mountain View, and completed his undergraduate education from University of Moratuwa, Sri Lanka.

Main supervisor
Silun Zhang, Assistant Professor, Division of Optimization and System Theory, Department of Mathematics, KTH.

Co-supervisor
Petter Ögren, Professor, Division of Robotics, Perception and Learning (RPL) at KTH.

About the project

Objective
This research aims to develop data-driven models for gait biomechanics to improve precision rehabilitation. By integrating statistical shape modeling, musculoskeletal simulations, and deep reinforcement learning, the project enables personalized gait impairment assessments and optimizes rehabilitation interventions for individuals with neurological and musculoskeletal disorders.

Background
Human gait is a complex biomechanical process influenced by neuromuscular control, skeletal structure, and external factors. Understanding gait abnormalities is essential for designing effective rehabilitation strategies. Traditional gait analysis, relying on motion capture and inverse dynamics, has limitations in scalability, personalization, and real-time applicability.

Recent advancements in artificial intelligence, musculoskeletal modeling, and wearable technology offer new opportunities for precision rehabilitation. Statistical shape modeling enables personalized bone and muscle geometry reconstruction, while deep reinforcement learning facilitates adaptive gait retraining strategies. This research integrates these approaches to develop predictive models that bridge the gap between clinical gait analysis and real-world rehabilitation applications.

About the Digital Futures Postdoc Fellow
Liangliang Xiang is a researcher in biomechanics and computational modeling. He holds a PhD in Bioengineering from the Auckland Bioengineering Institute, University of Auckland. His research focuses on gait biomechanics, musculoskeletal modeling, and explainable AI for movement analysis. He has developed predictive models for bone stress in running, integrated wearable sensors into biomechanical simulations, and applied deep learning for human movement analysis. He focuses on translating computational biomechanics into practical applications for gait rehabilitation.

Main supervisor
Elena Gutierrez Farewik, Professor, Department of Engineering Mechanics, KTH.

Co-supervisor
Ruoli Wang, Assistant Professor, Department of Engineering Mechanics, KTH.

About the project

Objective
The aim of this project is to explore and assess the potential of novel shape-changing wearables to improve body-based communication. These technologies hold promise because they can be worn on the body and provide tangible, haptic actuation that can emulate qualities of collocated physical interaction, as well as open up novel interactive qualities altogether.

A key concept that the project addresses is that of connecting bodies. I will explore how the interactive qualities of shape-changing wearables can be designed and used to foster a somatic connection between bodies, e.g., bridging together actions, perceptions and emotions from one body to another in a way that they are felt by the person, rather than just narrated. I envision that fostering this felt connection can, in turn, create richer, more effective and affective body-based communication.

Background
The digitalization of society, as well as recent global health developments (i.e. the COVID-19 pandemic), have fostered a shift from face-to-face, collocated interactions to remote communications. Communicating over video-mediated online platforms and conferencing software is becoming pervasive, shaping our everyday lives, practices, and how we interact with each other. Yet, these solutions do not adequately support settings where body-based interaction and physical contact are critical for effective, affective and rich communication, for example, remote health practices (e.g. remote physiotherapy) or affective well-being settings (e.g. long-distance relationships).

About the Digital Futures Postdoc Fellow
Laia Turmo Vidal is an interaction design researcher. Her research focuses on the design, development and evaluation of multi-sensory technologies that enrich the aesthetic perception of the body as a way to promote rich physical, emotional, and social experiences. Her research targets domains of health and wellbeing such as sports, fitness, rehabilitation and dance. Her research interests include wearable technology, material explorations, social cooperation and design methods development.

Laia holds a PhD and an MSc in Human-Computer Interaction from Uppsala University (Sweden) and a BDes in Multimedia Technologies from Universitat Politècnica de Catalunya (Spain). Prior to KTH, she was a postdoctoral researcher at i_mBODY Lab at the University Carlos III de Madrid (Spain). She has also been a research intern at UCL Interaction Center (UK) and a research visitor at the University of California, Santa Cruz (USA).

Main supervisor
Kristina Höök, Professor, Division of Media Technology and Interaction Design, KTH

Co-supervisor
Georgios Andrikopoulos, Assistant Professor at the School of Industrial Engineering and Management, KTH

Watch the recorded presentation at the Digitalize in Stockholm 2023 event.

About the project

Objective
This project aims to engineer the rules by which intelligent robots interact with each other in public spaces. In particular, this project focuses on tools from the field of mechanism design, which strives to set rules for interaction between rational agents. Designing rules via mechanism design allows for systems where robots collaborate toward global goals, even when their individual goals and specifications differ. Mechanism design, coupled with tools from formal methods and planning,  can help achieve global goals like safely sharing resources, minimizing the risk of failures in uncertain systems, and engendering the trust of humans.

Background
Multi-robot planning is a complex optimization problem that must consider both the goals of each robot and the interaction between those goals. The first step toward solving this problem requires understanding the realities of modern-day robots. The second step requires meta-reasoning through game theory. This problem becomes more complicated with the introduction of humans, who interact with robots in unique and often unpredictable ways.

About the Digital Futures Postdoc Fellow
Anna Gautier is a postdoctoral researcher in artificial intelligence and robotics. She works in the Robotics, Perception and Learning division at KTH Royal Institute of Technology. Anna obtained her PhD from the University of Oxford in July 2023, with a thesis entitled “Resource Allocation for Constrained Multi-Agent Systems.” Her research interests include multi-agent systems, human-robot interaction, planning under uncertainty, formal methods and mechanism design.

Main supervisor
Jana Tumova, Associate Professor at the Department of Robotics, Perception, and Learning at KTH Royal Institute of Technology, Digital Futures Faculty

Co-supervisor
Iolanda Leite, Associate Professor at the Department of Robotics, Perception, and Learning at KTH Royal Institute of Technology, Digital Futures Faculty

Watch the recorded presentation at the Digitalize in Stockholm 2023 event

About the project

Objective

The overall objective is to develop and evaluate AI-based and classical optimization (mathematical programming) approaches for sensor placement and control, data processing, communication, and motion planning.

This project aims to develop novel algorithms and computational methods for optimal planning, deployment, and operations of a network of AUVs and sensors in undersea environments. These algorithms will focus on optimizing the placement of sensors and movement of AUVs across the region, paying attention to relevant objectives including coverage and communication robustness. They will also focus on processing locally collected AUV measurements (e.g., sonar data) for situational awareness tasks (e.g., the emergence of an adversarial threat). The project will consider classical model-based approaches, AI/ML approaches, and hybrid approaches to developing these algorithms, comparing and contrasting them under different environmental settings and dynamics.

Of particular interest in this project is coordinating sensors and fleets of autonomous underwater vehicles (AUVs) to patrol regions of the ocean. However, these settings pose unique challenges in placement/motion planning such as limited communication, computations, and data processing. The communication challenges are mainly dealt with in a second sub-project led by the researchers at Purdue University. 

Background

Surveillance systems are becoming increasingly reliant on the ability of autonomous networked agents to conduct intelligence, surveillance, and reconnaissance (ISR) tasks. Much recent effort has been devoted to AI/ML-based approaches for augmenting such systems, though pinpointing exactly when AI/ML gives clear-cut advantages over traditional optimization and analytics-based is still an open question. Moreover, while many existing efforts in autonomy are focused on systems of drones, sensors, and other vehicles that operate above the surface, little attention has been paid to undersea ISR settings. The project focuses on the undersea setting, bringing together expertise from different fields, and forms a new line of collaboration between KTH, Purdue University, and Saab.

Crossdisciplinary collaboration

This project is part of a larger collaboration between Saab, KTH, and Purdue University. The project focuses on developing novel algorithms and computational methods for, planning, deploying, controlling, and operating a network of AUVs and different types of sensors over contested undersea environments.

The project brings together expertise from Applied Mathematics, Optimization, Electrical Engineering, and expertise in Underwater Environments.

Participating in the project:

Collaborators in the larger project: