Project name: Robotics and advanced industrial production
Project website:
ID code: CZ.02.01.01/00/22_008/0004590
Supported by: Ministry of Education, Youth, and Sports CZ (MEYS)
Programme: Operational Programme Johanes Amos Comenius (OP JAK)
Call No. 02_22_008 Excellent Research
Project duration: 06/2023 – 06/2028
Eligible costs: 467,9 mil. CZK
Principal investigator: prof. Dr. Ing. Zdeněk Hanzálek
Press Release 8 Feb 2024: CTU Acquires a Project for 180 Top Researchers in Robotics and Advanced Industrial Production

The ROBOPROX project focuses on breakthrough research and development in robotics and advanced industrial manufacturing by leveraging flexible deployment of robots with a high degree of autonomy, safe collaboration with humans, control and optimization of manufacturing processes, and computational methods for manufacturing and materials engineering. Cutting-edge research in this area will enable the development of more complex, modular and advanced solutions, and help increase the competitiveness of Czech industry. The project is interdisciplinary and promotes flexible development practices to meet changing customer requirements and respect increasing environmental constraints.

Read further:

WP1: Control and optimization for systems, materials and manufacturing WP2: Robotics and Computation Methods for Production Job Positions


The ROBOPROX project aims to create a growth pool of state-of-the-art research in robotics and industrial production by supporting excellent research teams in the field, enhancing current knowledge, creating original inventions, strengthening curricula of project partners, and leveraging national and international collaboration and research excellence. The aim is to transform Czech and European companies to provide more flexible, complex, competitive, and sustainable industrial production. Mathematical modeling, data-driven approaches, simulations, optimization algorithms, and formal methods are gaining wide acceptance in the industry. However, suitable tools are needed because there are no underlying concepts, linkable models, and high-performance algorithms. This need opens a clear opportunity for the ROBOPROX project to create an excellent new research environment for developing and deploying innovative research approaches in the industry.

  • Implementation of main research activities within two research work packages (WP)
  • Strengthening the R&D capacity by establishing and developing excellent research teams in the fields of robotics and advanced industrial production
  • Establishing new international collaboration
  • Strengthening the international collaboration of ROBOPROX research teams
  • Acquisition of instrumental and infrastructural equipment necessary for the implementation of research projects
  • Supporting the mobility of researchers – Implementation of the mobility programme


WP1: Control and optimization for systems, materials and manufacturing

This work package brings together experts in automatic control, optimization and materials engineering to offer a unique opportunity for interdisciplinary research collaboration in these domains. The work package targets fundamental advances in methods and the underlying theories as well as a rapid transfer of results to practice.

WP Leader: Ing. Milan Korda, Ph.D.

Research Areas (RA) / Research Objectives (RO)
RA1 Control of distributed-parameter systems and complex robotic structures (Tomáš Vyhlídal)
RA1 will develop control design tools for complex systems, including distributed-parameter and time-varying systems, and embed them in industry-ready low-complexity controllers and estimators. The methods developed will be applied to simultaneous motion control and vibration suppression of robotic structures, with applications in (micro)machining and laser-based additive manufacturing.

RA2: Control for modular systems, structures and materials (Michael Šebek)
RA2 will develop new model-based and data-driven automatic control methods for modular systems, structures, and materials. The primary focus will be on exploiting knowledge of interconnection structure as the enabling step for developing scalable, control-oriented modelling, simulation and analysis, as well as distributed and collaborative control, including hardware implementation. The target applications involve the coordination of multiple assembly machines and robots, as well as self-assembly processes.

RA3: Convex relaxations for non-convex problems in materials and industrial design (Didier Henrion)
is the WP’s theoretical backbone; it will develop methods for solving nonlinear and nonconvex optimization problems from materials engineering through a hierarchy of convex relaxations. It will guarantee the convergence of the hierarchy as well as ensure its scalability to industrial-size problems by exploiting the structure (sparsity, symmetry) inherent to these problems.

RA4: Computer-aided design, simulation and manufacturing of modular materials (Jan Zeman)
RA4 exploits modularity as the game-changing paradigm for distributed manufacturing of mass-customized products. As this modular-material framework is still in its infancy, novel theoretically supported algorithms and tools will be developed, exploiting modularity in simulating, optimizing and automated manufacturing.

RA5: Automation for nanoscale surface engineering (Tomáš Polcar)
RA5 aims at energy savings and cost reduction in materials engineering. This will be achieved by automated tribological testing, speeding up the development of new, ultra-low friction, materials; by the design and manipulation of 2D materials, paving the way toward the industrial use of solid superlubric materials; and by magnetron sputtering using a robotic arm for local deposition of thin films on large objects, reducing production time and material waste.

WP2: Robotics and Computation Methods for Production

This work package consists of collaborative research in robotics and advanced industrial production systems. It aims at fundamental advances in the underpinning theories and methods, including discrete optimization, machine learning, decision-making and verification, as well as an effective transfer of results into industrial practice.

WP Leader: prof. Dr. Ing. Robert Babuška

Research Areas (RA) and Research Objectives (RO)
RA6 Advanced robot autonomy (Libor Přeučil)
RA6 focuses on vision-based navigation for weakly-controlled environments without a dedicated navigation infrastructure. The research will lead to solutions addressing robustness, self-recovery from runtime failures and the ability to handle cases with high uncertainty, variations, and human presence.

      • Robot workspace modelling, robot under uncertainty (Karel Košnar)
      • Perception-based navigation using embedded workspace features (Libor Přeučil)
      • Long-term autonomy, fault detection and recovery (Miroslav Kulich)

RA7 Human-machine collaboration (Robert Babuška)
RA7 aims at making robots valuable work companions of humans. Current collaborative robots are not flexible, easily reusable or efficient. A modular architecture and knowledge base will be designed to overcome these problems. Novel approaches will be developed to represent demonstrated skills and tasks, and to schedule tasks between robots and humans, including different modes of robot autonomy. The system will also feature modules for interactive perception and multimodal human-machine communication.

RA8 Cooperative aerial robots for advanced industrial production (Martin Saska)
RA8 focuses on multi-robot autonomy in cooperative industrial production. Cooperative aerial robots (UAVs) can significantly improve future industrial production, e.g., by delivering components inside and outside industrial facilities. Currently, the deployment of UAV teams is limited by the quality of localization and mapping, flight speeds, and the efficiency of distributing tasks among a team of robots. Therefore, the focus will be on developing novel multi-robot mapping and localization techniques, motion planning for UAV agile flight in unknown dynamic environments, and on high-level mission planning for efficient deployment of multi-robot teams.

      • Topological multi-modal mapping and cooperative localization (Martin Saska)
      • Trajectory and high-level mission planning for agile multi-robot flight (Vojtěch Vonásek)

RA9 Resilient machines through continuous learning and sensing (Tomáš Svoboda)
RA9 researches machine learning to make the industrial deployment of robots more flexible. It will focus on weakly-supervised and self-supervised learning methods that respond to the enormous demand for human data annotation. Inspired by biological systems wherein intelligence is tightly connected with an organism’s body, concurrent and distributed reactive control will be researched in combination with whole robot body sensing. The new methods will make it easier for the system to adapt to new working environments, new sensors and new hardware.

RA10 Robotic routing in dynamic human-populated industrial environments (Jan Fajgl)
RA10 aims at higher efficiency and productivity in factory logistics and agriculture, using non-myopic planning and self-improving systems. The focus is on combinatorial sequencing and continuous optimization, augmented by the robot’s motion constraints. RA10 aims at quality guarantees with practical applicability in real-life deployments and method generalization to dynamic problems wherein the system’s performance can benefit from understanding long-term dynamics and online decision-making.

RA11 Scheduling, discrete optimization and decision-making (Zdeněk Hanzálek)
RA11 focuses on high-performance algorithms using graph theory, (meta)heuristics, mathematical programming, constraint programming, automated planning and machine learning. Attention will be paid to the novel extensions of production scheduling problems, bin packing, energy awareness, industrial communication scheduling and long-term autonomy decision-making. Both model-based and data-driven approaches will be considered in dealing with practical issues such as supply-chain disruption, personnel unavailability and parameter uncertainty.

RA12 Scalable formal methods in robotics and production (Mikoláš Janota)
RA12 will advance formal methods to enable scalable analysis and improvement of the software used in robotics and production in general. The scalability challenge will be tackled from the angle of static code analysis, automated reasoning, and theory. RA12 will focus on the development of novel approaches to symbolic execution, and code optimization supported by reasoning tools that automatically adapt and improve based on previous experience. Specific industrial problems will be tackled theoretically, anchored in the field of parameterized complexity.

RA13 Complex systems for flexible production (Vladimír Mařík)
RA13 develop methods for modelling, designing, and controlling manufacturing systems that allow a flexible response to changing production requirements through easy reconfiguration. RA13 will investigate multi-agent modelling to capture the behaviour of complex manufacturing systems and knowledge engineering methods, working towards fulfilling the vision of plug-and-produce. Transferable machine learning methods will be applied to reduce the training data requirements for manufacturing quality management systems.