About the project

Objective
Susan’s ride on Campus2030 aims to demonstrate the potential of digitalization in reducing the carbon footprint and improving the cost-efficiency of the construction and transportation industry. With this objective, the project will establish a one-of-a-kind smart road infrastructure demonstrator on the KTH campus Valhallavägen for the integrated design, construction and operation of smart infrastructures. The demonstrator will incorporate a digital twin of the KTH campus, corresponding to multiple models and data sets that enable virtual assessment and experience of the Campus infrastructure while being validated and updated through real-time data feeds from various sensors. Our work in this direction can be seen on our testbed for Intelligent Transportation Systems on www.adeye.se. Susan’s ride will showcase the potential of edge computing, federated learning, and digital twins in the digital transformation of road construction and autonomous vehicle path planning.

Background
Autonomous vehicles, dynamic charging of electric vehicles and vehicle-to-infrastructure communication are just a few examples that require a systemic solution to function sustainably. Making the smart road sustainable requires a partnership between road owners, operators, electricity companies, vehicle manufacturers, transport and logistics companies, and technology suppliers in digitalization. Data will become a fundamental asset in this partnership. They must be collected through a combination of new sensors in the infrastructure already upon construction on smart vehicles, including construction machinery.

Crossdisciplinary collaboration
The researchers in the team represent the School of Electrical Engineering and Computer Science, KTH, the School of Architecture and the Built Environment, KTH and the School of Industrial Engineering and Management, KTH. The project leverages and extends research carried out in the Campus 2030 project and the TECoSA research centre.

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About the project

Join the Second Drone Challenge at the Digital Futures Drone Arena, a one-of-a-kind interactive event with aerial drone technology! This year, the challenge focuses on moving with drones in beautiful, curious, and provocative ways – without needing to write a single line of code. The event takes place on May 16-17, 2023, at KTH’s Reactor Hall in Stockholm, Sweden. Read more about the challenge, the prizes, and how to sign up on our Drone Arena challenge website.

Objective
The Digital Futures Drone Arena is a concrete and conceptual platform where key players in digital transformation and society join in a conversation about the role and impact of mobile robotics, autonomous systems, machine learning, and human-computer interaction.

The platform is a novel aerial drone testbed, where drone competitions occur periodically to understand and explore the unfolding relationships between humans and drones. Aerial drones are used as an opportunity to create a foundation that lives past the end of this project. It is a long-standing basis for testing technical advances and studying, designing, and envisioning novel relationships between humans, robots, and their functioning principles.

Background
Few robot testbeds exist to experiment with application-level functionality. The Digital Futures Drone Arena bridges this gap by providing an easy-to-use programmable drone testbed for experimenting with novel drone applications and exploring the relations between humans and drones. The latter activity is driven by the concept of a ‘soma’ or the lived and felt body as it exists, moves, and senses the world. The theory provides an ethical stance on the soma, highlighting how technologies and interactions encourage certain movements and practices while discouraging others. As a critique of technology design and use, somaesthetics addresses the limited and limiting ways we sit at desks and tap away at keyboards. When we interact closely with drones, we must adapt to how we control them and move around them.

Crossdisciplinary collaboration
The researchers in the team represent the Connected Intelligence Unit, RISE and the Department of Computer and Systems Sciences, Stockholm University.

Articles:

Digital Futures Drone Arena

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About the project

Objective
The main aim of this project is to increase the diagnostic power of PET images for detecting lung cancer lesions at an early stage by overcoming the loss of contrast and spatial resolution caused by respiratory motion during data acquisition. Traditional ways of tackling this problem are computationally too demanding to be useful in clinical practice. For this reason, we will implement algorithms deploying a mixture of modelling of the data-acquisition set-up and machine-learning-based tools for image registration. The challenges of this project consist mainly in tackling the size of this four-dimensional image reconstruction problem and in being able to deliver images to radiologists in a time frame compatible with the hospital workflow.
In collaboration with our clinical partners at Karolinska Hospital in Huddinge, we will collect gated PET line-of-response activity data and corresponding 3D CT attenuation maps of the thorax region of 400 patients and test and optimise our 4D algorithms and gating strategies on this data. We will evaluate the resulting reconstructions with a particular focus on their diagnostic power for small lung lesions and make the reconstruction software package openly available.

Background
PET (Positron Emission Tomography) is a medical imaging modality that reconstructs the 3D distribution of metabolic activity by detecting photons emitted during the in vivo annihilation of free electrons with positrons from an injected radioactive tracer. In principle, cancer lesions will be visible in the reconstructed image with high contrast compared to the surrounding healthy tissue thanks to their peculiar metabolic fingerprints (e.g. higher sugar metabolism). PET is, in fact, one of the most powerful imaging modalities for cancer diagnosis and staging. However, the long acquisition time required to collect projection data with an acceptable noise level leads to motion artefacts that strongly affect the contrast-to-noise-ratio of lesions. This is of particular interest when trying to detect tumours that are of a size comparable to the system resolution (~5 mm) and that are continuously moving because of respiratory motion. Those lesions are the most important ones to detect for early-stage diagnosis, leading to a better prognosis for the patient.

Crossdisciplinary collaboration
This is a project in which state-of-the-art mathematical research for solving large inverse problems meets the clinical practice of medical imaging and brings together faculty from the Department of Mathematics at the SCI school at KTH with faculty from the Department of Biomedical Imaging of the CBH school.

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

About the project

Objective
This project aims to develop and study a demonstrator of semi-automated online math tutoring by combining a human approach and automated tutoring. The automation will be based on Natural Language Processing analysis of previous tutor-student interactions. To evaluate the semi-automated tutoring concept, we will conduct a randomized controlled trial by randomly assigning students into a treatment group where tutoring is semi-automated and to a control group with only human online tutoring. We will also conduct a thematic comparative analysis of student-automated tutor interaction compared to student-human tutor. The findings will contribute to research on semi-automated tutoring, a scientific area that has received limited attention. The semi-automated approach could greatly impact society because tutors can help more students. The project will serve as an example of beneficially using semi-automation without losing the human touch of tutoring.

Background
It has been known for decades that one-to-one tutoring is a very effective teaching method, although the key challenge is to scale it up. Maths Coach Online (mattecoach.se) has been offering one-to-one tutoring by teacher students to K-12 students using chat and interactive whiteboard since 2009 and has conducted more than 70,000 tutoring conversations. We are transforming Maths Coach Online into a national service, making scaling to a larger volume of students important. Therefore, we want to explore how to support high-quality math learning for as many students as possible by incorporating semi-automated intelligent tutoring.

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Industrial Engineering and Management, Department of Learning in Engineering Sciences and the KTH School of Electrical Engineering and Computer Science, Department of Intelligent Systems.

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

Aboyut the project

Objective
This project aims to develop an open-source ROS-compatible real-time logic-based integrated planning, reasoning and control system for mobile robots. The key novelty in our project is including non-axiomatic reasoning in the robot software stack to complement common techniques such as deep learning in handling uncertainty. The system will be featured in a scavenger — a mobile robot used to inspect a city-like environment to carry out a collection of pieces of waste. With the final demonstrator, we aim to showcase the potential of our integrated planning, reasoning, and control system for mobile robots that need to carry out tasks in unknown environments.

Background
Today’s robotic control systems rely on big data, machine learning approaches and/or extensive (physical) modelling and behaviour pre-programming to achieve their required functionalities. While still utilizing such techniques, this demonstrator aims to introduce improvements towards low-energy, cost-efficient and effective mobile robots by integrating a reasoning-based system, the Non-Axiomatic Reasoning System (NARS). NARS is designed to build mission-relevant hypotheses from a stream of input events and to act upon the most successful predicting hypotheses. With its ability to learn and update hypotheses in real-time with little training or task pre-programming, NARS will be the key technology allowing our robot to improvise in challenging and uncertain situations, identify new types of objects and categorize them based on their perceived properties.

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Electrical Engineering and Computer Science, Division of Robotics, Perception and Learning and KTH School of Industrial Engineering and Management, Department of Machine Design.

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

About the project

Objective
The project objectives:

Background
Micromobility refers to lightweight, typically electric, transportation modes like bicycles, e-scooters, e-bikes, and similar small vehicles. To simulate riding experience in micromobility, researchers typically design and construct indoor simulators to advance research, development, and understanding of the behaviour of micromobility riders.

While many bicycle simulators have been previously developed and investigated in terms of safety, realism, and motion sickness, other micromobility simulators still need to be explored. Particularly, four new types of micromobility are slowly growing within urban spaces: e-scooters, segways, electric unicycles, and one-wheeled skateboards. This opens new research opportunities focused on improving the safety of riders and understanding their behaviour in urban environments. This demonstrator project is focused on addressing how a VR micromobilty simulator can be designed to accommodate the current and future micromobility vehicle innovations and what the implications are for research and innovation in the domain of VR simulators.

To investigate this research question and understand riders’ behaviour on four types of micromobility vehicles safely, one indoor stationary Virtual Reality Micromobility Simulator (MicroVRide) will be designed and built. Different riding modes concerning four types of vehicles will be accommodated under safe and controlled conditions by this simulator. The design and construction of this simulator will involve the utilization of two main elements: (1) a fixed hardware platform (with a dismountable handlebar) with tracking of body weight distribution and (2) Virtual Reality (VR) simulation (presented in a wireless VR headset) to experience a virtual world. Following the construction of MicroVRide, experiences with users of different levels of riding proficiency will be investigated to understand their riding behaviour, performance, and experience.

The development of MicroVRide is motivated by a commitment to advancing VR simulator technology, establishing a secure and realistic environment for safe vehicle practice.

Crossdisciplinary 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
Based on empirical data from sidewalk robots’ trips, we will shed light on sidewalk mobility and improve real-world robot delivery operations. Through statistical analysis and Machine Learning (ML), we will assess the efficiency of robots’ paths and their relation to pedestrian infrastructure, interactions with different transport users (such as walkers, cyclists, e-scooters, and motorized vehicles), and other variables (e.g., weather).

A crucial task of the project will focus on integrating prediction models with routing algorithms to discover more effective routing solutions. Another task will involve identifying Walkability KPIs” to describe sidewalk mobility conditions based on the data collected.

Background
Sidewalk robots appear to be a promising solution for City Logistics. Hubs, retail locations, and even retrofitted vehicles might dispatch them for short-range trips and partially replace standard, less sustainable delivery methods. The ISMIR project aims to develop a more comprehensive understanding of sidewalk robot delivery in realistic scenarios. The investigation of sidewalk navigation challenges will also provide the opportunity to explore pedestrian infrastructure and sidewalk mobility from a novel perspective.

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Architecture and the Built Environment, Department of Urban Planning & Environment, and KTH School of Electrical Engineering and Computer Science, Department of Intelligent Systems.

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