Mechatronics and Autonomous Systems Research Group
Mechatronics is one of the most dynamic technical trends in the world and represents the synergy of IT, electronics, optics, and mechanical systems. The research activities of the Mechatronics and Autonomous Systems Research Group are directed at the further development of the field.
The Mechatronics and Autonomous Systems Group's research activities focus on further development of mechatronics and autonomous systems. Modern vehicles (including various electric vehicles, e.g., electric cars, unmanned land, and aircraft) also require energy efficiency optimization. The research team is developing several test platforms and digital twins to achieve this goal. The possibilities of combining real and virtual sensors with artificial intelligence are being explored to prolong the working life of vehicles and reduce the risk of failure.
Additionally, the main focus of R&D is the development of hardware and related software based on artificial intelligence for robotics and automation control systems and developing user interfaces for systems, sensing, and especially new machine vision applications. The emphasis is on industrial robotics and the development of unmanned aerial vehicle (UGV) and unmanned aerial vehicle (UAV) systems, as well as hardware-in-the-loop simulation and test systems.
The mechatronics and autonomous systems research group offer expertise, consultations, training, and research partnerships.
CURRENT
ADVANCED DIGITAL TOOLS TO ACCELERATE THE DEVELOPMENT OF SOFTWARE-DEFINED ELECTRIC VEHICLES
The project aims to advance Electric Propulsion Drive System (EPDS) Digital Twin (DT) technology for Software Defined Electric Vehicles (SDEVs), with a focus on achieving DT adaptive and intelligent levels. It addresses the need for efficient testing and evaluation of electric propulsion systems in line with EU clean energy transition goals. Leveraging the rapid development of DT technology, the project seeks to contribute to SDV technology through enhanced modeling, data gathering, IoT integration, and system optimization. Key challenges include lifecycle management, data processing, and real-time communication between physical and virtual systems. The project encompasses advanced modeling, data gathering, IoT, and communication infrastructure, system integration, optimization, and technology demonstration.
01.01.2025–31.12.2029
ETISCAREER MANAGEMENT SERVICES FOR EUROPEAN TALENTS
The CROSS project aims to strengthen the European Research Area by developing innovative tools to mainstream the new Charters´ principles, fostering organisational change and career interoperability. Key outputs include a Self-Assessment Competence Tool, a comprehensive Roadmap for transversal skills training, a Mentoring Handbook, a Roadmap for career counseling, an Intersectoral Collaboration Handbook, and an HRS4R Repository. These form a comprehensive set of career management services, which will be piloted and implemented across four intersectoral networks. The development of these resources will follow a co-creative process, engaging stakeholders across Europe, facilitated through the creation of our ResearchComp Community of Practice. This approach ensures their adaptability to diverse European ecosystems. A key component of CROSS is the creation of a platform designed to support institutions in obtaining the HRS4R award. This platform can also serve as a shared resource for all projects funded under this call, ensuring their continued use and expansion beyond the project’s lifecycle. By promoting organisational change, CROSS will benefit research-performing organisations and researchers at all career stages, significantly improving career prospects and delivering broader societal impact.
01.06.2025–31.05.2028
ETISENGINEERING ACADEMY
The Engineering Academy is a project initiated by the Ministry of Education and Research and funded by the European Social Fund, with the goal of improving the quality of engineering education and reducing the labor shortage in technical fields. The project is led by the Education and Youth Board and is joined by five higher education institutions.
The Engineering Academy includes 22 engineering-related study programs, of which ten have been selected as priority focus programs for development.
The project has three focus areas:
- Increasing the number of applicants
- Improving the quality of education and Increasing alignment with labor market needs
- Reducing dropout rates
The Technical University has set a goal to increase admissions in the field of engineering by 15% each year. To improve the quality of education, the action plan includes a significant expansion of project-based and problem-based learning, curriculum development, quality enhancement, and infrastructure upgrades. Additionally, lecturers’ training and the recruitment of teaching assistants are planned. To reduce dropout rates, individual support for students will be increased, both during the first year and when completing their final theses. First-year students will also be offered additional mathematics courses. The goal is to significantly reduce dropout rates and increase the number of graduates.
01.05.2023–31.08.2029
ETIS
FINISHED
DIGITAL TWIN FOR PROPULSION DRIVE OF AUTONOMOUS ELECTRIC VEHICLE
Autonomous driving is no longer just an idea of technology vision, instead a real technical trend all over the world. The continuing development to a further level of autonomy requires more from energy optimization. The optimization of electric propulsion drive systems of self-driving electric vehicles by using autonomous and monitoring sensors are not often discussed. The goal of the proposal is to develop a specialized unsupervised prognosis and control platform for such energy system performance estimation. This goal requires the development of several test platforms and digital twins. A digital twin is composed of three components – the physical entities in the real world, their virtual models, and the connected data/view that ties the two worlds together.
01.01.2020–31.12.2024
ETISRETRAINING OF FOSSIL FUEL MINING AREA WORKFORCE FOR MODERN INDUSTRY
Taking into account the relatively low automation and robotization of the traditional fossil fuels industry, a significant retraining is necessary for the workforce in transition to modern technological industrial sectors. This has to be done, in order for the potentially available workforce to meet the needs and requirements set by the modern mechatronics oriented industry, which actively implements the Industry 4.0 ideology. To meet those requirements, the project proposes retraining courses for the soon-to-be-available workforce. The topics covered by the retraining are electrical drives, automation, robotics, power electronics, and condition monitoring of industrial systems. These separate fields are strongly interconnected and overlapping, and together with the connection point of IT technologies, they can be considered the main technological pillars of modern mechatronics oriented industry.
01.11.2021 - 01.11.2024
MAIN PAGE
ETIS
REMAKER moodle pageDEVELOPMENT AND INNOVATION OF ICT MODULES IN THE FIELD OF TECHNOLOGY
This objective of the EEV5040 Industrial Automation and Drives activity is to introduce students to the importance of industrial automation and electric drives and the latest trends in these fields (including IoT). As a result of the development activities, students will acquire in-depth knowledge of electric drive management, model-based design methodology and IoT applications in industrial automation in the future. They are able to create, adapt and analyse electric motor control systems and solve real problems in this field. These results influence the quality of industry-specific ICT teaching, providing students with the practical skills and knowledge essential to today's industrial automation.
Within the framework of the development project, the subject EEM0040 Machine vision is also amended, where the traditional machine vision curriculum is added to it by adding the hyperspectral technology component. The aim of the development activity is to combine concepts of machine vision with hyperspectral data processing. Within the subject, students will develop practical skills in the use of hyperspectral cameras, from camera setup to processing of various hyperspectral images. They acquire knowledge of the specifics of hyperspectral data, such as the wavelength spectrum and its relationship to the properties of materials. In addition, they learn the methods of machine vision, which allow to identify different objects and characteristics from hyperspectral images.
01.12.2023–30.11.2024
ETISDIGITAL PLATFORM SUPPORTING REMOTE LABORATORY CLASSES IN ELECTRICAL ENGINEERING, MECHATRONICS AND AUTOMATION
The project is primarily aimed at counteracting the negative impact of the COVID-19 pandemic on the academic education process at technical universities. In accordance with the restrictions and with an aim to prevent spread of the virus many universities made a decision to transfer all studies, including practical classes, at fully online from. Due to that fact that practical classes were either not implemented at all or were carried out in a very narrow and simplified (impractical) manner. The goal of the RELABEMA project will be the development of a set of laboratory exercises in mechatronics and electric drive and their integration into a commonly used e-learning tool, which is the Moodle platform.
01.03.2021–28.02.2023
ETISINDUSTRIAL INTERNET METHODS FOR ELECTRICAL ENERGY CONVERSION SYSTEMS MONITORING AND DIAGNOSTICS
Modern energy systems, such as wind turbines, motor drives in industry, and electric vehicles are prone to failures, resulting in loss of production, unavailability of services, or environmental disasters in a worst case. Electrical, mechanical, and thermal stresses are directly or indirectly responsible for these failures. To prevent these issues, energy systems must be regularly checked through routines and schedule specified by the manufactures. This schedule-based condition monitoring approach provides very little information on the remaining lifetime of the devices and does not allow for their prognostic and full exploitation. Furthermore, it is costly and presents problems related to the fact that devices might fail in between the routine check, which causes environmental risks and unsustainable use of resources.
In this project, we present solutions for these drawbacks by combining Virtual Sensors (VS) with powerful Artificial Intelligence (Al) tools. We will develop models of the underlying devices that can run in real time and thus serve as Virtual Sensor fed by real operation data from the actual devices. The VS will monitor thermal, mechanical, and electrical stresses. The data from the VS will be used in failure models to predict the remaining lifetime of the devices allowing for fault-tolerant and overload usage of the said devices, as well as condition-based maintenance. This is possible if the models are used in combination with Al or machine learning engines running in the clouds. The data for training the Al-engines will be generated from physical models of the devices, such as the finite element models of electrical machines, or in some cases from reduced models of these devices, to speed up the learning process. We expect the methodology to detect localized failure potentials in critical components, such as bearings, gearboxes, motors and generators. The possibility to apply the methodology to power electronic devices will be investigated.
01.01.2021–31.12.2023
ETIS
The lab offers machine vision systems development, expertise, consultancy, research partnerships as well as training.
Keywords: machine vision; image processing; robotics; industrial automation; expertise; training.
Contact: Daniil Valme
daniil.valme@taltech.ee
The e-Vehicles laboratory provides calculations, modelling, testing, development, expertise, consultancy, training, and research partnerships for electric drives.
Keywords: electric drives; motor control; electric vehicles; expertise; training.
Contact: Anton Rassõlkin, Mahmoud Ibrahim
anton.rassolkin@taltech.ee, mahmoud.mohamed@taltech.ee
Keywords: industrial robotics; robot; training; robotics; robotics, robotics, robotics, industrial
robotics, robotics, robotics, robotics, robotics.
Сontact: Anton Rassõlkin
anton.rassolkin@taltech.ee
We offer prototyping, workshops, and research partnerships.
Keywords: 3D printing; soldering; prototyping.
Contact: Martin Sarap
martin.sarap1@taltech.ee