Key areas of research and development
- Classical optimisation algorithms – application in production, planning and control (linear and non-linear optimisation, operations research, Bayesian inference)
- Machine learning and image processing – applications in quality assurance, work planning and planning processes
- Mobile robotics – applications in logistics and production (use of reinforcement learning, control engineering/systems theory, simulation methods)
- Sensor Technology and Data Analysis in Production and Logistics (Data Science using R and Python, production simulation)
- Technical process modelling – (algorithmic support for process engineering in the steel industry)
- Process development, modelling and automation (BPMN, workflow engines)
- Machine learning for medical image processing (segmentation algorithms, data science)
Prof. Dr. rer. nat. Martin Gürtler
Chair of Production and Logistics Systems
Institute
TPMB | Institute for Technology and Production in Mechanical Engineering
Research Profile
Robotics, Control & AI
Telephone: +49 (0)341 3076 4129
Email: martin(dot)guertler(at)htwk-leipzig.de
Team
- M.Sc. Hannah Großer
- Dr rer. med. Daniel Kruber
- Dipl.-Ing. Lukas Kube
- Dipl.-Chemiker Sebastian Löbner
- B.Eng. Karl Marbach (student research assistant)
- M.Eng. Eric Plaß
- M.Sc. Caroline Schmidt
TALOS
Development of a framework for user-friendly control of logistics robots
The TALOS project is developing a framework that utilises models trained and developed in a simulator to make the control of humanoid robots considerably easier. These models enable the robots to learn and carry out specific tasks independently, without the need for prior human demonstrations. The resulting task-oriented programming allows these systems to be used without in-depth prior knowledge of the underlying concepts.
Funding: ESF-Plus, State Innovation Fund
Cooperation partner: Prof. Dr André Ludwig, University of Leipzig, Faculty
of Economics Contact: M.Sc. Eric Plaß
Project duration: 01/2025 – 12/2029
EUSOF
Smart positioning for tram trials
The ZIM project focuses on developing an innovative method for precise positioning during tram test runs. The aim is to improve the accuracy of vehicle positioning by utilising various signal sources such as GPS, speed signals and inertial sensors. The correct combination of these data sources plays a key role, particularly during test runs where the vehicle’s position must be recorded continuously and reliably.
Funding: ZIM programme of the BMWK
Contact: M.Sc. Benedikt Schablitzki
Project duration: 06/2024 – 09/2025
PRO-EAF
Data-driven modelling of an electric arc furnace
The aim of this project is to develop a data-driven model of the electric arc furnace (EAF). This model is intended to enable predictions of the temperature and oxygen content of the molten steel during tapping, i.e. at the end of the melting process. These predictions take into account uncertainties arising from data collection and process modelling.
Funding: Elbe-Stahlwerke Feralpi Riesa
Contact: M.Sc. Caroline Schmidt
Project duration: 11/2023 – 10/2025
APrOS
Anatomically Preformed Orbital Surgical Instruments
The aim of this project is to improve surgical procedures on the orbit by using specially designed instruments that are adapted to the complex anatomy of the orbit. Orbital wall fractures, which are often caused by accidents such as blows or falls, lead to aesthetic and functional problems, such as double vision or restricted eye movement. Every year, thousands of patients in Germany require surgical treatment; however, outdated instruments are frequently used, which are not ideally suited to this type of surgery.
Funding: BMWK
Contact: Dr rer. med. Daniel Kruber
Collaboration partners: Forschungs- und Transferzentrum Leipzig e.V.; Anton Hipp GmbH
Project duration: 06/2023 – 05/2025
