Here you can find current open projects and theses from our research group. We also supervise theses proposed by students, provided that the topic fits into the portfolio of our research group. For more information, please contact the supervisor in charge. Last Update: June 29th 2022.

Bachelor Theses

RoboCupJunior Soccer Simulation (Lehramt Informatik)

RoboCupJunior ( bietet seit über 20 Jahren die Möglichkeit junge Menschen mit Robotikwettbewerben für Naturwissenschaften und Technik zu begeistern. Die Verein RoboCupJunior Austria und die Technische Universität Graz richten seit 2009 die nationale Meisterschaft aus und unterstützt Schulen, Lehrer*innen und Schüler*innen bei der Teilnahme an den Bewerben.

Bedingt durch die Pandemie konnten in den 2019 und 2020 keine physischen Veranstaltungen durchgeführt werden. Daher wurden viele Aktivitäten auf Online-Events umgestellt. Ebenso wurde der RoboCupJunior online durchgeführt. Die teilnehmenden Schulen konnten entweder über Telekonferenz ihre Roboter vorstellen oder an einem simulierten Wettbewerb für Roboter im Bereich Rescue oder Soccer teilnehmen. Dazu wurde ein eigener Simulator entwickelt, mit dem man Roboter-Fußball-Spiele online simulieren kann (

Ziel dieser Bachelor-Arbeit ist es, ein Konzept zu entwickeln wie der RoboCupJunior Soccer Wettbewerb und die Simulation in Schulen genutzt werden können, um einen zeitgemäßen und interessanten Informatikunterricht zu gestalten. Der Vorteil des Online-Wettbewerbes ist, das die Roboter-Hardware als Hürde wegfällt. Im Zuge der Arbeit sollte auch versucht werden, eine Art Boot-Camp und Testturnier mit Schulen zu organisieren. Diese Arbeit kann auch von einem Team aus 2 Studierenden bearbeitet werden.


Usability Evaluation of Explainable Artificial Intelligence (XAI) in Robotics

In order to make technical systems such as robots more intelligent, various concepts and methods of artificial intelligence are usually applied. This intelligence can be conveyed in symbolic (human-readable) representations of problems, logic and tasks (e.g. logic programming, expert systems) or by means of data-driven machine learning approaches such as decision trees or neural networks. Many of these methods are commonly reffered to as black box, because the underlying process is hard to understand. Explainable Artificial Intelligence (XAI) aims to provide explanations for the decision process in order to make the system more understandbale. This thesis aims to investigate the explainability of typical concepts of intelligent robots. For this purpose, literature research is to be conducted to identify relevant information provided by the concepts and to evaluate the usability of this information to generate explanations. Furthermore, the usability of existing XAI approaches is to be investigated to improve the understandability of the identified robotic concepts.  


Learning Tools and Migration Aid for the new Robot Operating System ROS2

The Robot Operating System (ROS) is an open source set of software libraries and tools for building robot applications. From drivers to state-of-the-art algorithms, and powerful developer tools, ROS provides a framework for the easy integration of robotics software. The ROS software is the main use for the development of robotics applications, a successor framework called ROS2 is the new standard in the robotics community. Due to the wide use of the first version of the framework and the young age of ROS2, published packages are often designed only for the previous version. In addition, fundamental changes have occurred between versions, so that one must learn a new understanding for its use. The goal of this work is to address these two problems. For this purpose first a teaching concept for the new ROS2 Framework is to be provided with which the new functionalities can be obtained. Afterward existing packages of the old version are to be converted exemplarily around the necessary migration steps to examine and identify.  


Seminar/Master Projects

Porting of Autoware Stack

Autoware is an open-source implementation of a complete automated driving stack. It provides a complete tool chain for mapping, localization, and navigation for automated vehicles. It is build up on ROS 2. In this project we are interested to port the Autoware stack to the Mercator robot platform of the AIS research group and to evaluate its performance in automated navigation in urban environments. The main tasks of the project are to become familiar with the Autoware stack and to implement and configure the interfaces to the actual sensor and platform setup of Mercator. If there is interested the project can be extended towards a master's thesis.


Master Theses

Mobile Manipulation in Disaster Response

In this master thesis manipulation skills for a mobile manipulator in disaster response scenarios will be developed. Skills are for instance the opening of a door or the operation of a valve. The skills will be embedded into the software framework of a mobile manipulator of a research project to improve trust and transparency in human robot interaction. The skills can be either fully automated or can be executed semi-autonomous with help of the user (e.g. user identifies the door handle and forwards this information to the robot). The challenges of this master thesis lie the combination of perception and controlling a robot arm in a complex movement. The thesis will be carried out in cooperation with Prof. Justus Piater from University of Innsbruck who is an international expert in learning of complex motion patterns of robots arms.


Control Architecture for Long Term Autonomy

In this master thesis we are interested what control architecture is needed if a robot is operating for a long time autonomously, like months and years. The key challenge is that the during such an extensive deployment issues like software/hardware failures or failing actions will happen for sure, the environment will change (seasons, reconfiguration) and the robot system will wear and thus the robot have to deal with all this issues autonomously. In order to achieve this perception, monitoring the system and its activities, managing of goals and desires, and decision making needs to be well integrated. The goal of the thesis is to design and implement a prototype control architecture able to operate a robot safely and without manual intervention for a long time. A long term evaluation of the architecture in a task like exploration, delivery or inspection for one month without manual intervention is envisioned.


Software Engineering Workflow for Autonomous Robots

Autonomous robots are complex systems comprising a number of hardware and software components and showing quite complex behaviors. While in other domains like aerospace or automotive elaborated software frameworks and processes are well established this is hardly realized for the autonomous robot domain. In this thesis we like to investigate what good software development practices exists in other engineering domains and develop a concept for such a software development process to produce better and more reliable software. In particular we like to look into requirement engineering, automate testing of robots and its software, verification and software product lines. This thesis will be conducted in cooperation with Prof. Bernhard Aichernig who is an international expert for formal methods in software engineering.


Reinforcement Learning for Offroad Navigation

Automated navigation in offroad environments such as alpine areas or stone deserts is a challenging task. Usually a layered approach with a stack of a global path planner, a intermediate trajectory planner and a trajectory following controller is used. The task trajectory controller is to steer the vehicle along a basically feasible trajectory trough the environment. Local conditions and difficulties such as medium sized rocks, different surfaces (e.g. sand, gravel), slip, slopes, and uneven ground needs to be considered for successful and safe execution of a trajectory. In this thesis it should be investigated if such a controller can be learned using reinforcement learning. The envisioned use case is the Mecator robot platform that navigates in an alpine environment with various terrains. Starting from simulation a basic controller architecture and learning approach will be developed and trained. Once such a controller is obtained in simulation the results will be transferred to the real robot system in order to safe time and resources in training. The thesis is conducted in cooperation with Eduardo Veas from the Institute of Interactive Systems and Data Science.


Intelligence Test for Autonomous Robots

In classical test theory (aka intelligent test) one aims to estimate the capabilities of individuals on a relative (or absolute) scale using a set if items (tasks) and the rate of solving items by the individuals. The test theory is a well established methodology and uses probabilistic approaches in order to consider the nondeterministic nature of task solving (lucky guesses and careless mistakes). The construction of good and discriminative items with different defined difficulty is a main challenge in this context. While this is well researched in the context of humans there is little work in the context of autonomous robots. We are interested in such items designed for robots because we also want to rate the capabilities of autonomous systems (e.g. robot A is more capable than robot B) but also for predicting  how well a robot will perform in a new environment and task  (new item). In this thesis in cooperation with researchers from psychology we like to develop an approach and a set of test items for tasks of autonomous robots such as navigation or manipulation in order to be able to apply test theory from psychology to robots. In a simulated environment proper items (environments and tasks) will be generated (automatically) and rated according to their difficulty for different robots. This set of validated items can later be used to rate and compare the capabilities of the different robots. This aspect is of increasing interest in the context if the certification pf autonomous systems.


Explanations in Semantic Path Planning

To navigate a robot within its environment, geometric path planners such as A* or Dijkstra are fundamental tools which are widely used. Other planning methods which focus on non-geometric representations such as topological planners or semantic planners are being scientifically researched, but are not yet applicable to general practice. The reason often given for this is the difficulty of representing the planning space, as well as the lack of benefits compared to geometric planners. This work focuses on highlighting and exploiting the advantages of semantic path planners such that they can be used in real-world applications in the near future. By creating a semantic environment and using a semantic path planner, generated paths can be explained in human-like manner to the user in natural language. In case of failures, issues of the environment and/or the path can be communicated to non-expert users helping them to understand the funcionality of the robot. Additionally, using semantic planning, alternative routes and explanations for not taking certain routes can be made transparent. Using the advantage of naturally explaining paths to the user aims to increase trust into the system and to reduce the operators cognitive workload during operation. 



Prof. Dr. Gerald Steinbauer-Wagner