Theses

We are always happy to work with students who want to write their thesis at our chair. On this page you will find an overview of the current thesis topics that are available for collaboration, as well as a list of topics that have already been completed. If you have a topic that fits our research profile, we would be happy to talk to you about the possibility of a collaboration.

Open Theses

Enhancing Robot Navigation and Coverage Tasks by Moving Obstacles Autonomously

  • Abstract:

    Robots are increasingly used in unstructured environments, such as homes and factories, where they are required to navigate the environment reliably and efficiently. Among other tasks, mobile robots are expected to perform coverage when it comes to tasks like cleaning, inspection, or the likes. Common metrics for coverage tasks are the time it takes to cover the area, the distance traveled, and the percentage of the area that has been covered. Current robots struggle at navigating in particularly cluttered environments, where they drive suboptimal trajectories to avoid obstacles and, in the worst case, they get stuck due to the lack of space to drive to the next goal and trigger recovery strategies to free themselves. The aim of this thesis is to extend the coverage task with obstacle interaction, to allow the robot to push selected obstacles a few centimeters when they prevent the robot from cleaning efficiently with a good coverage. Additionally, the implemented method should be integrated into a robotic platform and tested in a real-world scenario.

Finished Theses

Speeding up reinforcement learning via abstractions of complex environments

  • Abstract:

This study investigates whether abstracting complex environments (making the continuous state space discrete) can speed up reinforcement learning. A complex environment was abstracted with multiple approaches, to see if it gives a benefit over training without abstractions. It was concluded that the approaches we tried are insufficient at speeding up the training process at a satisfying percentage, while also losing precision. We propose that abstractions should not be constructed on the environment manually, rather the agent should get precise observations and the abstraction ability of the neural network should be improved.