About the project

Objective
The Demonstrating Rich and Batteryless Human-Powered Interaction using Backscatter Communication (HumanScatter) project focuses on designing and prototyping interactive systems that are limited in energy usage to our bodily capabilities – technology driven by human power. No battery-free Bluetooth transceivers can directly receive data from unmodified Bluetooth devices such as smartphones. This project will investigate this challenge in combination with human-powered interactions with the ambition to create sustainable, robust, and resilient low-power technology and communication mechanisms. The goal is to achieve rich, battery-free, human-powered interactions using backscatter communication. The project will result in a new Bluetooth backscatter transceiver, multiple novels and intriguing demonstrators of human-powered interaction using backscatter techniques, and a final refined, fully working demonstrator suitable for long-term exhibition at the digital futures demo space.

Background
Throughout history, people have mostly relied on their bodily capacity, leading a human-powered or self-powered lifestyle. In contrast, today’s energy abundance has led to a replacement of human power by invisible centralized electricity production, enabling people to easily use magnitudes more energy than provided by the bodily limits of the individual. Backscatter communications and associated techniques offer a promising alternative to traditional communication technologies by lowering their power consumption so that they can often be powered only by radio waves or other energy-harvesting methods. While backscatter Bluetooth transmissions have been widely demonstrated, battery-free reception of Bluetooth packets remains challenging.

Crossdisciplinary collaboration
The researchers in the team represent the School of Electrical Engineering and Computer Science, KTH and the Department of Computer and Connected Intelligence Unit, RISE.

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:

Abouyt the project

Objective
This demo project will develop a prototype for mid-sized haptic seated interactions in collaboration with Volvo Cars. The demo will exemplify how to prepare drivers to dis -and re-engage with self-driving cars through the seat, create posture awareness and increase comfort for drivers. This is an urgent need for our collaborators at Volvo Cars, which impacts the safety and feasibility of semi- and automated cars on the roads. The prototype will be designed using novel haptic technologies and interaction design techniques that develop and strengthen a mutual collaboration between the user and the system. The project will result in technical innovation in mid-level haptics and the construction of design knowledge for an orchestrated meaningful touch of the body.

Background
Semi-autonomous cars are a growth area. Designing interactions within self-driving cars that are able to re-engage drivers in a driving issue in a timely and safe manner continues to be an open question for manufacturers and researchers. This project will demonstrate the feasibility and efficacy of using mid-sized haptics embedded within a car seat to re-engage and disengage a driver in a semi-autonomous car setting.

Crossdisciplinary collaboration
The researchers in the team represent the School of Electrical Engineering and Computer Science, the School of Industrial Engineering and Management at KTH and RISE.

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

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:

About the project

Objective
This project aims to support planning for universal access to clean cooking and contribute to Sustainable Development Goal 7 (SDG7) on Affordable and Clean Energy by radically integrating artificial intelligence (AI) methods into the open-source geospatial clean cooking model OnStove. To enhance OnStove, the project proposes implementing geospatial AI learning models to improve the spatial understanding of current technologies used for cooking and generate quasi-optimal transitions over an extended modelling period, considering as well behavioural issues. To achieve this, open-access geospatial information is combined with ground information gathered through available survey data. Finally, all comes together as a new open-source, user-friendly interface that will enable a broader audience to use the tool and ensure a long-term community of practice around clean cooking access modelling.

Background
Approximately 2.3 billion people still lack access to clean cooking worldwide, making them rely on traditional and polluting fuels. This practice poses challenges as households spend significant time collecting and cooking with traditional fuels, impacting women and children. Cooking with polluting fuels causes severe health consequences, estimated at around 3.2 million premature deaths annually from respiratory diseases. Moreover, traditional cooking exacerbates climate change and causes deforestation around the globe. On previous efforts, the first open-source geospatial tool for assessing and comparing the net benefits of various cooking solutions, OnStove, was developed. The tool was applied in the first study for sub-Saharan African countries, using Geospatial Information Systems (GIS) to evaluate cleaner cooking solutions in each square kilometre of the region. The tool considers benefits such as reduced morbidity, mortality, greenhouse gas (GHG) emissions, and time saved while factoring in investment costs, fuel purchase, and operation and maintenance.

By calculating the relative differences between current fuel stoves used and cleaner alternatives, OnStove determines a transition’s net benefit (benefits minus costs) and selects the options providing the highest net benefit in each square kilometre. Decision-makers can use the outputs to understand the impacts of achieving clean cooking access, including estimates on deaths avoided, time saved, GHG emissions avoided, health and GHG emissions costs avoided, total costs, and affordability constraints. OnStove facilitates decision-makers in identifying areas for action, guiding market strategies, and prioritizing investments to promote diverse clean-stove options in low- and middle-income countries. The tool has gained policy community interest, aiding energy access planning in Nepal and Kenya. It is also integrated into global initiatives like the World Resources Institute’s Energy Access Explorer and the International Energy Agency’s Clean Cooking Outlook Special Report.

Crossdisciplinary collaboration
Our project benefits from the expertise of a diverse team from the Department of Energy Technology and the Department of Sustainable Development, Environmental Science and Engineering at KTH Royal Institute of Technology. Furthermore, we collaborate closely with international organizations active in the clean cooking domain, such as the Clean Cooking Alliance (CCA), the World Resources Institute, The International Energy Agency, and Sustainable Energy for All. This expert group ensures that we have the technical and practical background to develop an innovative solution that addresses the project’s challenges and supports achieving SDG7’s clean cooking goals.

About the project

Objective
We propose energy-efficient, high-speed, real-time monitoring of complex traffic scenarios in urban areas. Reliable real-time information about urban traffic supports emergency responders and city planners and reduces carbon emissions, thereby increasing the urban citizens’ quality of life. Using decentralized neuromorphic sensing and processing, we obtain four benefits compared to state-of-the-art: (1) low latency, (2) minimal transmission bandwidth, (3) low power, and (4) enhanced privacy.

As a result of the project, we expect to provide a Live Demonstrator which, in real-time, identifies objects in traffic (e.g., cars, bicycles, and scooters) while continuously running on a negligible energy budget (e.g., a solar cell).

Background
In the upcoming decades, urban areas will become “much larger, more complex, and interconnected, ” requiring optimized infrastructure with increasing automatization at increasingly larger scales. More broadly, real-time information about traffic and infrastructure utilization will become an increasingly valuable commodity for companies and officials to adapt and update the infrastructure as needed. Such data is generated by video cameras in cities today.

Embedding low-power event cameras with low-power neuromorphic local computation provides a uniquely scalable solution for the growing need to monitor urban traffic and resource flows in real time. Our project estimates a 100x reduction in power and a 20x reduction in installation cost.

Crossdisciplinary collaboration
The teams contribute expertise in neuromorphic hardware and algorithms for system development and expertise in traffic observation and urban traffic management. Only both teams and their complementary expertise can develop real-time low-power computing systems for the societal relevant task of urban traffic monitoring.

About the project

Objective

Background
Despite the enormous investments in automated vehicles, there are still challenges regarding safety and security. Moreover, open research testbeds are lacking to address these challenges. The CAVeaT project will address those needs.

The project will leverage advances and resources made available from industrial partners, from the TECoSA edge-computing and 5G testbed, and from the ITM and EECS schools at KTH, including the AD-EYE platform and an adversarial attack pipeline for autonomous driving simulation.

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Industrial Engineering and Management and the KTH School of Electrical Engineering and Computer Science.