Semester Projects, Thesis Topics & Summer Jobs

Planning & Optimization | Mobile Robotics | Industry and Research | Novel Technologies | Finished Projects

We offer topics from various fields of robotics to students of bachelor’s and master’s studies. Our students solve tasks such as navigation, localization, planning for one or more robots, or processing information from rangefinder sensors and standard and RGBD cameras. We work with both simulated data and real robots, from classic manipulators, through mobile robots to drones. In the laboratory, we also have Tiago++ – a two-armed mobile manipulator. The topics are listed so that they can be followed up with a bachelor’s/master’s thesis in the following semester.

Note: The listed topics may be reserved, so always check with our staff. It is also possible to come up with your own work assignment after prior consultation with our staff.

Leaflet with Semester Projects, Thesis Topics & Summer Jobs

Updated 2023-05-19


Contact person: Miroslav Kulich (

We focus on the fundamental and applied research on planning problems in robotics and artificial intelligence. Two main streams of our research are routing problems and multi-agent path finding. Our solutions combine knowledge from different scientific disciplines: artificial intelligence, computational geometry, graph theory, operations research, combinatorial optimization, and robotics.

Mission Planning in Polygonal Domains

Contact person: Jan Mikula (

This discipline combines task and path planning for mobile robots as well as 2D geometry and optimization. The tasks include inspecting a known environment or specific regions within it or searching for a hidden object of interest. The ultimate objective is constructing the most efficient feasible path that enables the robot to complete the selected task in the shortest possible time.

Multi-Agent Path Finding

Contact person: David Zahrádka (

Imagine a warehouse where hundreds of robots are transporting goods from one place to another. How do they avoid collisions and reach their destinations efficiently? This is the problem of Multi-Agent Path Finding, where the goal is to find collision-free paths for a team of agents. In this project, you can design and implement algorithms for pathfinding in dense environments. Another problem is the development of robust execution methods, which ensure safe and effective execution of planned paths on real-life robotic fleets in the presence of unexpected delays.

Metaheuristics and Optimization Challenges

Contact person: David Woller (

We design metaheuristic algorithms – custom approximation algorithms used for addressing challenging NPhard combinatorial optimization problems that are beyond the capabilities of exact solvers. Each year, our student teams successfully participate in international optimization competitions from various application sectors, such as truck fleet loading optimization, power transmission network maintenance scheduling, or electric vehicle routing.


Robotic autonomy encompasses a diverse range of applications and problems. We aim to develop and improve advanced methods in various fields, including autonomous navigation, mapping, exploration, and movement control.

Teach-and-Repeat Navigation

Contact person: Tomáš Pivoňka (

One of our research topics is the development of a teach-and-repeat navigation system for ground and aerial mobile robots. The system is primarily designed for long-term autonomy applications that require high robustness to various changes in the environment. The related topics for the final thesis include tasks from computer vision, sensor fusion, robust control, and software development. Furthermore, many tasks provide an opportunity to work with real robots.

Autonomous Transportation Systems for Smart Cities

Contact person: Libor Přeučil (

Robots play a key role in future smart cities, facilitating autonomous transportation, delivery, and surveillance. These systems rely on onboard intelligence for safe driverless navigation with minimal external infrastructure. Student projects may cover collision avoidance design, visual mapping for outdoor navigation, and implementing teach-and-repeat autonomy on shuttle buses, with subsequent testing and evaluation.

Semantic-Oriented Navigation

Contact person: Viktor Kozák (

We address object-oriented mobile robot navigation, a crucial aspect of intelligent robotics. Our research aims to develop advanced algorithms and techniques for object detection, localization, and object-based robot navigation. By leveraging cutting-edge AI technologies, we enable mobile robots to autonomously navigate in dynamic environments, accurately detect and localize objects, and perform visual inspections.

F1Tenth Autonomous Racing Competition

Contact person: David Zahrádka (

Develop a 1/10 scale autonomous racing car for the international F1Tenth racing competition. At IMR, you can join one of two F1Tenth CTU teams that finished 4th out of 43 teams in 2022. You can develop LiDAR and camera-based localization, mapping, and control algorithms. The goal is to drive through the racing track safely and, at the same time, as fast as possible!


Our laboratory collaborates with numerous industrial partners whom we help with challenging real-world robotic applications. Thus, students not interested in purely research topics can gain practical experience by working on these.

Autonomous Electric Vehicle Battery Disassembly

Contact person: Karel Košnar (

As electric vehicles become increasingly popular, recycling their batteries and components becomes essential for various economic and environmental reasons. This project deals with motion planning for the autonomous robotic fleet that transports discarded batteries and their parts between different disassembly stations.

Mobile Robotic Platforms

Contact person: Jan Chudoba (

We focus on controlling various mobile robots with special kinematics, which enable better maneuverability or passage in confined spaces or are structurally simpler. This is advantageous in numerous industrial and research applications. Different chassis designs with omnidirectional movement capabilities require the development of new motion control methods that effectively utilize expanded kinematic possibilities.

Detection of objects with high visual uncertainty

Contact person: Lukáš Bertl (

This project is an extension of the completed industrial project "Autonomous Robotic System for Non-Destructive Testing", which sought to automate the inspection of manufactured components utilizing ultrasonic and eddy current testing methods. The system's workspace is equipped with a robotic arm that includes an integrated camera and simulated probe. The project provides two research streams of possible student topics. In the first stream, students will have the opportunity to tackle a variety of machine vision challenges, such as developing innovative algorithms for object detection and classification on the component, enhancing the precision of object localization, and identifying manufacturing defects. The second research stream focuses on the visual localization of the component itself. Students can utilize an array of visual-based sensors — camera, depth camera, or LiDAR. The sensor is mounted to the robotic arm and it is used to determine the component's precise positioning in reference to the robot. A key aspect of this research involves comparing the precision of various sensors and the algorithms employed. This involves measuring and creating point clouds, as well as merging and processing these data sets to align them with known model of the component. Students are encouraged to harness the capabilities of modern C++ or Python, leveraging powerful libraries such as Tensorflow, PyTorch, OpenCV, and ROS2, among others, to drive their research and development efforts forward.


As a part of our research, we also focus on the development of novel technologies. We create innovative robotic platforms with diverse kinematics and design new end-effectors for industrial projects. Our interests also extend to developing inspection and infrastructure technologies, specifically related to mobile robotics and smart manipulation.

Smart Materials and Self-Assembly

Contact person: Lukáš Supik (

We cooperate with the FCE CTU on basic research of meta-materials. Our focus is on the self-assembly of modules forming a meta-material. This self-assembly is based on the mitigating of chemical reactions on macro-scales. We develop simplistic modules, which can chaotically move and mechanically bind together. Students can work on driving control of modules and path planning.


The Intelligent and Mobile Robotics Group has a history of more than 25 years conducting both basic and applied cutting-edge research, and numerous bachelor and master thesis projects.

Navigation for a Two-Handed Mobile Manipulator TIAGo++

Navigation of the TIAGo++ robot in multi-floor environments, including elevator usage for inter-floor movement. Implementation of existing methods for singlefloor navigation and development of elevator usage strategy based on object detection and visual navigation. Successful achievement of floor-to-floor movement and dynamic map adaptation.

TIAGo++: A Robotic Archer

Development of a robotic archer using TIAGo++ robot equipped with a camera for target detection. Mathematical model simulation, trajectory design for low motor torque, target detection, aiming, and shooting processes implemented. Experimental evaluation of trajectory, target detection, and shooting components.

Local Outdoor Navigation of a Mobile Robot by Terrain Traversability Mapping

Autonomous navigation of mobile robots in unknown environments, including simultaneous localization and mapping (SLAM) techniques such as the iterative closest point (ICP) algorithm. This project addressed traversability evaluation based on robot properties and geometric features. Implementation of ICP SLAM for navigation, including traversability analysis, pathfinding, and path following. Testing was performed in Gazebo simulator and real city environment.

Search for a Static Object in a Known Environment

Locating a static object in a known 2D polygonal environment using a mobile robot with a 360° sensor. Design and implementation of an efficient search strategy using innovative discretization and metaheuristic techniques. It has been confirmed that the proposed method surpasses existing approaches in terms of solution quality. Additionally, it remains competitive in terms of computational time.

Close Enough Travelling Salesman Problem in a Polygonal Domain

Solving the Close-Enough Traveling Salesman Problem (CETSP) in circular-shaped areas. Introduction of a novel General Large Neighborhood Search (GLNS-CETSP) method combining shortest path and point-circle-point algorithms with GLNS and Touring Circle Problem algorithms. Comparative experiments demonstrated improved results in shorter computation time.

Trajectory Planning for a Heterogeneous Team in an Automated Warehouse

Trajectory planning for a cooperative team of robots in an automated warehouse environment. Adaptation of planning algorithms to specific warehouse requirements, including modifications of existing plans. Development of a fleet management system with human-worker integration. Implementation and comparative evaluation.

Page last modified on April 08, 2024, at 05:26 PM EST
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