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:

About the project

Objective

The overall goal of the project is to develop optimization frameworks to assist in process design, learning accurate models from process data, and support optimal decision-making. The optimization algorithms and frameworks will be tailored towards the needs and processes of interest for LKAB. The type of optimization problems considered in the project falls into the category of “Gray-box” optimization, where parts of the optimization problem are known analytically, but some parts are given by a simulator. The simulator is not necessarily a complete black box, but the simulation model can be too complex to be integrated directly into the optimization model.

Designing industrial processes is a challenging task and often requires the use of advanced design and simulation software. For example, accurately describing the process and physical phenomena involved often requires the solution of systems of partial differential equations (PDEs) or a Computational Fluid Dynamics (CFD) simulation. Therefore, evaluating the impact of simply changing a single design parameter can require running a CFD or solving a large system of PDEs and may also require access to chemical and thermodynamic libraries. Solving such systems is a challenge on its own, and in practice, requires the use of advanced simulation software. The main downside of such software is the inherent black-box nature; you can evaluate a single specific design choice at a time, but the end-user does not gain more knowledge. In practice, when such software is used for process design the engineers often follow some common rules of thumb and trial and error to come up with a good design that can be evaluated by software and later put into production. However, the resulting design might be far from globally optimal, i.e., there might exist a far more superior design. Employing such a sub-optimal solution can, e.g., result in increased environmental impact due to a higher energy consumption, increased raw material usage and waste. Furthermore, when used in investment planning and feasibility studies, the sub-optimal designs can cause superior technologies/solutions to seem unreasonably expensive and maybe even economically infeasible. Therefore, there is a strong need for frameworks that enable the combination of simulation software with optimization algorithms to find optimal process designs. 

Background

LKAB is an international mining and minerals group that supplies sustainable iron ore, minerals and specialty products. Since 1890, the company has evolved through unique innovations and technology solutions and is driven forward by more than 4,500 employees in 12 countries. The company is the largest supplier of iron ore in the European Union and a key player in the transformation of the iron and steel industry towards sustainability. LKAB’s goal is to develop carbon-free processes and products by 2045.

The work to eliminate carbon emissions creates new challenges and opportunities. Potential routes and new ideas are continuously being investigated and evaluated. As the production processes are complex and experiments often expensive and time-consuming, the feasibility and potential of new alternatives are often investigated numerically through computer simulations. Currently, computer simulations are used in a black-box fashion, i.e., you can evaluate a specific configuration of process parameters, but you don’t gain any more information or knowledge of the best configurations. Therefore, it is of utmost importance to utilize the full potential of the models and to find the best solutions. This project aims to provide a key component for a systematic model-based design approach.

Increasing computational power, increasing amount of data, and overall digitalization are continuously boosting the potential benefits of extensive computational simulations in process design, but how to best utilize simulators is not trivial. To learn and extract knowledge from the process simulations, the project aims to develop deterministic optimization methods and frameworks for determining optimal designs and operations.

Crossdisciplinary collaboration

The project is a collaboration between LKAB and KTH, where we are focusing on developing optimization algorithms suitable for tackling challenging optimization tasks in industrial production processes. The project brings together expert knowledge from Process Systems Engineering, Applied Mathematics, Optimization, and Process Simulation.

Participating in the project:

About the project

Objective
This project aims to develop and evaluate algorithms for dynamic inference offloading in Scania’s fleet, optimizing the trade-off between model accuracy, latency, and resource consumption. The proposed cascaded inference approach ensures that data is first processed by small models at the edge (M1). Depending on inference confidence, decisions are made in real-time on whether to offload tasks to more complex models (M2-M4) with higher accuracy but increased computational costs.

The core objectives include: (1) designing algorithms that improve inference accuracy while minimizing latency and bandwidth usage; (2) providing theoretical guarantees for the proposed methods, particularly in terms of regret minimization; and (3) benchmarking these algorithms using real-world data from Scania’s operational vehicles. The research will contribute to optimizing deep learning model deployment, ensuring scalable and efficient AI integration in industrial applications while maintaining cost-effectiveness and system reliability.

Background
Deep Learning (DL) models have become a standard for data-driven tasks such as classification and predictive analytics due to their high accuracy. However, their computational and memory demands often require cloud-based deployment, which introduces challenges like latency, bandwidth consumption, and security concerns. In response, edge computing has gained traction, enabling inference on resource-constrained Edge Devices (EDs) such as IoT sensors, mobile devices, and autonomous vehicles. While edge deployment reduces communication delays and enhances data privacy, small models often suffer from lower accuracy.

Scania, a global manufacturer of commercial vehicles, faces similar challenges in deploying DL models across its fleet for tasks such as autonomous driving, predictive maintenance, and sustainable operations. There exists a trade-off between accuracy and efficiency when placing models at different computation points. This project explores cascaded inference, where small models operate locally, and only complex cases are offloaded to more powerful computing resources, balancing accuracy with cost efficiency.

Partner Postdocs
This project brings together experts from multiple disciplines to address the challenges of deploying DL models efficiently across Scania’s fleet. Researchers from machine learning, optimization, embedded systems, and automotive engineering will collaborate to develop cascaded inference strategies that optimize accuracy, latency, and resource usage.

Scania’s senior data scientists, Dr. Sophia Zhang Pettersson and Dr. Kuo-Yun Liang, provide real-world insights into vehicle data, predictive maintenance, and cost modelling. Associate Prof. Lei Feng adds knowledge in Bayesian optimization and deep learning techniques for edge computing.

This collaboration ensures that theoretical advancements in machine learning align with practical deployment challenges in commercial vehicles. By integrating perspectives from academia and industry, the project fosters innovation in scalable AI solutions, leading to efficient, adaptive, and cost-effective DL deployment across connected fleets.

Supervisor
KTH researchers, led by Prof. James Gross, contribute expertise in hierarchical inference, algorithm development, and performance guarantees.

About the project

Objective

The ALARS project aims to drastically improve how Unmanned Underwater Vehicles (UUVs) are deployed and recovered in maritime operations. Currently, UUV launch and retrieval rely on manual, time-consuming, and high-risk methodsinvolving surface vessels and human intervention. ALARS introduces an autonomous aerial solution that integrates drone (UAV) technology with UUV operations, significantly enhancing efficiency, safety, and scalability in underwater missions.

Key features of ALARS:

By automating these processes, ALARS aims to reduce human risk, increase mission success rates, and unlock new capabilities in maritime security, environmental monitoring, and offshore industries.

Background

Modern UUV operations are essential for naval intelligence, surveillance, reconnaissance (ISR), environmental monitoring, and subsea exploration. However, traditional deployment and recovery methods rely on mothership-based handling, which presents multiple challenges:

ALARS directly addresses these limitations by integrating aerial robotics with autonomous underwater systems, allowing for:

By leveraging Sweden’s expertise in robotics, AI, and autonomous systems, ALARS sets a new global benchmark for efficient and safe maritime operations.

Crossdisciplinary collaboration

The ALARS project brings together experts in:

The project is a collaboration between KTH, Saab Kockums, and SMaRC, with direct industry involvement to ensure real-world validation and future deployment.

Principal Investigators (PIs)

About the project

The SHARCEX project focuses on improving underwater operations by integrating advanced autonomous underwater vehicles (AUVs) with human divers. The goal is to enhance safety and efficiency in extreme underwater environments such as defense, rescue operations, and law enforcement.

Key technologies being developed and integrated include:

The project progresses through multiple phases:

  1. Development of AI models
  2. System integration
  3. User interface design
  4. Operational testing

KTH leads the project in collaboration with FMV and Saab, aiming to demonstrate robust AUV-Diver collaborations validated through extensive simulations and experiments.

Background

Underwater environments are among the most challenging operational domains due to their unpredictability, extreme pressure conditions, limited visibility, and communication constraints. Human divers working in these environments face significant safety risks, particularly in defense, search-and-rescue, law enforcement, and infrastructure inspection scenarios. While Autonomous Underwater Vehicles (AUVs) have been deployed in these fields, their potential remains largely untapped due to limitations in real-time adaptability and human interaction.

Current AUV systems often operate independently, following pre-programmed missions with limited real-time decision-making capabilities. This restricts their usefulness in dynamic and high-risk situations, where divers must quickly adapt to changing conditions, assess threats, and make complex decisions. There is a clear need for AUVs that can function as intelligent, real-time assistants, enhancing human capabilities rather than merely executing pre-set tasks.

The SHARCEX project addresses this gap by developing a next-generation human-robot collaboration frameworkfor underwater operations. By integrating AI-driven AUVs with human divers, the project aims to create a synergistic system where both human and machine leverage each other’s strengths.

Crossdisciplinary collaboration
The project integrates expertise from multiple fields:

Collaboration partners:

Principal Investigators (PIs)

About the project

Objective
RECOPS aims to provide evidence-based insights into the benefits of open-source software in the power sector. The project focuses on identifying impactful areas for open-source applications, such as HVDC control, distributed renewable energy (DER) simulation, and hardware operations. Through case studies and a novel assessment methodology, RECOPS will evaluate system robustness and cost benefits from open-source approaches. The project is structured into three work packages, incorporates stakeholder engagement, simulations, and interdisciplinary collaboration.

Background
Open-source software has gained widespread use in industries like robotics and general software engineering but remains underutilized in the power sector. While academic initiatives exist, their limited maintenance and lack of industry relevance hinder broader adoption. The power sector’s need for robustness and focus on proprietary, closed software solutions further constrain open-source integration. However, there could be benefits with open-source software to enhance cost-effectiveness and robustness in energy systems.

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Industrial Engineering and Management, Unit of Energy Systems and the KTH School of Electrical Engineering and Computer Science, Division of Electric Power and Energy Systems.

About the project

Objective
A Lego-inspired design framework called SiLago (Silicon Lego) enables automation from the system level to ready-to-manufacture solutions for high-performance Edge AI applications. This framework bridges the gap between ease of use and performance by providing ASIC-comparable efficiency while achieving significantly improved energy efficiency—10X to 100X better than commercial off-the-shelf (COTS) solutions such as GPUs and FPGAs. The research project aims to enhance the SiLago framework to support comprehensive system-level implementation by addressing computation, storage, and interconnect requirements. These enhancements will enable SiLago to streamline the synthesis of complex applications, such as those required in industrial use cases. Finally, the improved system-level capabilities will be seamlessly integrated with the existing application-level synthesis flow, creating a unified, automated design process from applications to manufacturable silicon.

Background
The field of electronics and VLSI has driven transformative advancements in computing, enabling the development of increasingly powerful and efficient hardware systems. System architecture plays a crucial role in defining the structure and interaction of hardware components, ensuring efficient computation, storage, and communication. Despite these advancements, designing high-performance and energy-efficient hardware, such as ASICs, remains a complex, resource-intensive process requiring specialized expertise. The SiLago framework builds on these foundations, combining principles of VLSI, hardware modeling, system architecture, and design automation to provide a modular, automated solution for ASIC design.

About the Digital Futures Postdoc Fellow
Nooshin Nosrati completed her doctoral research in Digital Electronic Systems at the University of Tehran (UT). Her doctoral thesis was on hybrid reliability provisions in embedded systems with a focus on Computational Elements. Her research interests encompass hardware design and modeling, computer architectures, reliability and testability of embedded systems.

Main supervisor
Ahmed Hemani, Full Professor, Department of Electrical Engineering, KTH.

Co-supervisor
Artur Podobas, Associate Professor, Division of SCS, School of EECS, KTH.