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ARIS – Automation, Robotics, and Intelligent Systems

Devices, machines, control systems, robots – artificial systems – have progressed from simple task execution – to systems that interact directly with humans, comprehend a complex situation, collaborate autonomously, make assessments, learn, evolve, and adapt.

The ARIS research group aims at applied research within automation, robotics, and intelligent systems for a range of applications in collaboration with industry and the public sector. The group combines expertise in control engineering, modelling and simulation, electronics, communications, mechanical systems, robotics, machine learning, and Artificial Intelligence.

Some of the thematic topics and applications we address are:

  • Self-learning systems and personalized solutions for Health applications
  • Robotics and automation for the industry
  • Industrial process monitoring, simulation, and control
  • Internet of things (IoT) and advanced solutions for next generation industry
  • Materials and equipment fatigue, remote monitoring and predictive maintenance
  • Self-adapting energy optimization in buildings
  • Robotics and fleet optimization for maritime applications
  • Computer vision and cognitive systems
  • Modelling and simulation for industrial (Digital Twin) and health applications

Projects

Self-learning systems for Assisted Living Technologies

Interdisciplinary research project: the Assisted Living Project (ALP) – Responsible Innovations for dignified living at home for people with mild cognitive impairment or dementia (MCI/D) . The ALP project conducts research within Machine Learning, Information and Communication Technology, Health sciences, Social sciences, and Ethics. Our contribution to this project focuses on Activity Recognition and Cognitive Systems using Machine Learning. The ultimate aim of our research work is to develop self-learning and self-adapting systems that can assist people with MCI/D. Such solutions shall be applicable more generically to any situation where the human is somehow in distress and can benefit from assistance by an intelligent system. Data is obtained from smart home sensors in a field trial with real users, as well as in the lab. We use both discrete on/ off sensors (motion detectors, magnetic sensors, power sensors, etc.) and 3D data obtained from depth sensors (Computer Vision). We employ probabilistic models, Artificial Neural Networks, as well as explore data fusion methods for our prediction models.

Funding: Norwegian Research Council

Project duration: 2016-2019

Participants: OsloMet, Sensio AS, The Norwegian Board of Technology, Oslo Municipality, RoomMate AS, University of Bristol, Karlsruhe Institute of Technology

Autonomic Systems – Applied Automation and Artificial Intelligence

Strategic Lighthouse project in the Faculty of Technology, Art, and Design – in collaboration with all Engineering Departments in the Faculty. The aim of the project is to leverage Automation and Artificial Intelligence and apply it to a selection of sectors across engineering disciplines. Applications cover both industry and the public sector. The project builds on the key expertise of the three Engineering departments – in particular Electrical Eng., Mechanical Eng., Civil Eng., Building Physics and Environment, and Computer Science – and aims at establishing specialized solutions with high socioeconomic impact in close collaboration with external partners. The combined knowledge generated from the project will have both high relevance and high transfer value across sectors and applications. Current activities include indoor navigation in robotics, self-adapting monitoring of vital signals, and adaptive energy-efficient buildings.

Process Simulation and Learning Technologies

Simulator training is widely used in different industries and in academia as a teaching tool. An expert instructor guides the trainees through different normal and/or abnormal scenarios using a dynamic process simulator and gives feedback during and after the scenario. The aim of our research is to enhance simulator training by providing uniform the technical feedback to the trainees and by enabling independent simulator training prior to team training. In the current PhD research work, an automatic online feedback system is developed, tested and evaluated.  

DigiFab: Automatisert digitalisering og veikart mot Industri 4.0 for SMB-fabrikker

The project focuses on developing services, cost-effective tools and methods that support SMEs in moving towards Industry 4.0, and beyond, by using available as well as emerging technologies and strategic methods to create a roadmap for Industry 4.0. The project will demonstrate affordable solutions for developing digital twin simulation models using autonomous vehicles and platforms. 

Funding: Norwegian Research Council

Project duration: 2018-2021

Participants: OsloMet, NTNU, SINTEF Digital, SINTEF Raufoss Manufacturing, Q3 Partners, SDPD-PLM Consult

OASYS - Ocean-Air synoptic operations using coordinated autonomous robotic SYStems and micro underwater gliders

The OASYS project will develop and demonstrate an innovative type of fully automated Ocean-Air coordinated robotic operation that has the potential for drastically reducing the cost of ocean observing systems. The project proposes the development of a swarm of low cost Micro Underwater Gliders (MUGs) that can operate autonomously with the support of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vessels (USVs) for deployment, recovery, battery charging, and communication relay. The system reduces human intervention to the minimum, revolutionizing the affordability of a broad range of surveillance and data collection operations.

Funding: H2020 ERA-NET Cofund MarTERA

Project duration: 2018-2021

Participants: OsloMet, NTNU, Norwegian Polar Institute, TriOS GmbH

Industry 4.0 Lab

The Industry 4.0 laboratory in the Faculty for Technology, Art and Design (TKD) will demonstrate Industry 4.0 concepts to be used in practical teaching and learning, as well as function as a platform for research related to Industry 4.0, Industrial Internet of Things, and Big Data. The laboratory includes a hybrid plant and industrial robot systems. The hybrid plant combines continuous processes and manufacturing process. The industrial robotics equipment simulates an automated manufacturing process using robotic manipulators and autonomous ground vehicles