Reseach topics
We actively seek for a cooperation with students at all levels of their studies. Come and join us to work on challenging, reallife oriented problems. We offer to students various research topics for their thesis and projects. Some of them are more industry orientated (i.e. working on a task required by some of our industry partners, solving their reallife problem), others are more theoretical (i.e. focusing on developing new fundamental algorithms and theory).
In the list below you can see a short description and motivation for solving the problem/project. After the student chooses the area of his/her work, the topic is more deeply specified. If you still hesitate, just make an appointment with sojkam1fel [dot] cvut [dot] cz (Michal Sojka) (@wentasah) or Přemysl Šůcha (suchapfel [dot] cvut [dot] cz) and come to see what we are working on.
Energy consumption optimization of robotic cells The fourth industrial revolution is a term for a new way of designing, realizing and controlling modern (smart) factories. One of the principles of this “revolution” is virtualization of plants which is supposed to allow efficiently design future smart factories. The aim of the thesis is to design and implement an algorithm for optimizing energy consumption of robotic cells (see the figure) using a virtual model constructed in Siemens Process Simulate. 

Machine learning for production data analysis The data available in the information system of the production company are often incomplete. Namely absence of the data like processing time and changeover time makes it nearly impossible to construct production plans and schedules. On the other hand, there is often a correlation between a production order in hand and a similar one in the historical data comprising real production time measured at the factory floor. In order to capture this correlation, we aim to use Machine Learning techniques. Due to our collaboration with industry, the data from the real production will be used to derive processing times and changeover times in order to optimize production. 

Scheduling with uncertain parameters Most of the existing scheduling models assume that all parameters are given exactly by specific numbers. However, in reality, this is often not a realistic assumption. Therefore, more practical or missioncritical applications require to incorporate uncertainties. In this topic, you will take a part in the development of new efficient algorithms for such problems with uncertain parameters. 

Autonomous model car F1/10 Selfdriving cars are future of public transportation and a lot of skilled engineers will be needed to make this technology safe and reliable. This topic could be your entry to this future world. The goal is to improve the algorithms in our current car and participate in F1/10 challenge with that car. In addition to that, we collaborate with industrial partners on the development of real autonomous cars and the techniques developed here may be ported into real cars. 

Fundamental research in scheduling & open problems Sometimes, even the fastest computers are not able to solve the given combinatorial problems in a reasonable time. This does not necessarily mean that these problems are impossible to solve, but a deeper theoretical understanding of the problem is required to implement efficient algorithms. The task of this topic is to study fundamental properties of the given scheduling problem and implement efficient algorithms that exploit these properties (we recommend this topic to more mathematicallyinclined students). 

Integrate project and manpower scheduling In a collaboration with Gent University in Belgium, we propose a topic addressing scheduling of complex projects (e.g. construction of building complexes). This problem assumes not only allocation of the project activities but also scheduling of human resources necessary to realize the project. The objective is to develop a scheduling algorithm for this bilevel optimization problem. 

Production scheduling Would you like to solve practical and challenging problems faced by companies such as SKODA, Proctor&Gamble and EATON? Then production scheduling is a right topic for you. The task is to study a given production scheduling problem and implement an efficient algorithm for it. Depending on the specific setting, different objectives and constraints have to be considered, e.g. smoothing of the energy consumption, rescheduling due to unexpected disturbances, etc. To evaluate the proposed algorithms, we use an industryproven simulation and visualization tool Plant Simulation which is developed by Siemens. 

Parallel algorithm for combinatorial problems Combinatorial optimization is not only a mathematical domain but it also plays an important role in many applied areas like logistics, project management, production, communication, etc. However, for many realworld combinatorial problems, the solution search trees become unmanageably large. One way how to mitigate this problem is to use of parallel algorithms. Therefore, the topic addresses development an efficient parallel (GPU) algorithm for solving a realworld combinatorial problem. 

EATON Lab projects EATON offers a large number of industrial projects related to embeded devices that will be applied to modern manufacturing facilities. Find out more here. 

Mixedreality with Microsoft Hololens Join us on a project for developing new applications for mixedreality headset Microsoft Hololens. Applications (interactive holograms) are programmed in Unity 3D engine that runs an onboard computer. The target application will help human operators to interact with production machines and HMI panels manufactured by EATON company. The work is done in cooperation with EATON Lab. 

Machine learning for energy consumption analysis Noninvasive sensors such as a sensor of the electrical current is an elegant way how to improve observability in a production environment. However, signals from these sensors may be difficult to understand and analyze. To address this, we work on machine learning algorithms to compute uptime of the machines, their mode and time needed to perform the given operations. We collect a large stream of data from the production shopfloor into our data cluster that needs to be automatically labeled and processed by advance machine learning algorithms. 

Reliable hardware for autonomous cars Future selfdriving cars will require not only tremendous computational power for processing data in realtime but also increased reliability and faulttolerance. While sufficient performance is easily provided by modern hardware, the reliability of such hardware is not considered in "automotivegrade". This topic addresses the challenge of increasing the reliability and determinism of Xilinx UltraSCALE platform, used by our industrial partners in their autonomous cars, by implementing socalled "Predictable Execution Model" on this platform and improving it by using new hardware features of the platform. 

Scheduling of the TimeTriggered communication on Deterministic Ethernet The Ethernet technology is one of the most widespread technology in the world. Nowadays, the network has the usage even in the areas where it was not intended. It becomes more and more common even in highly critical applications, where any failure can cause jeopardy of life, because of the increasing bandwidth demands of e.g. autonomous driving systems. The car or airplane manufacturers endeavor to modify the Ethernet protocol to be also capable of the deterministic and hard realtime communication  TTEthernet, 802.1Qbv, 802.1Qbu and 802.3br. In such a timetriggered Ethernet standards, the communication follows a schedule known in advance. Design and development of an algorithm, which is able to create efficient schedules, is the main objective of this diploma thesis topic. 
Courses
Industrial Informatics Research Center leads the Computer Engineering study branch of popular study program Open Informatics at the Faculty of Electrical Engineering of CTU in Prague. For more information check out the site for Computer Engineering [in Czech]