Mobile Robotics

In the programme, students learn the foundations to develop intelligent robots that can understand and model their surroundings, navigate the world, and act smartly. The study programme is strongly research-oriented and aims at educating the newest generation of top talents in robotics. It focuses on probabilistic and learning techniques enabling mobile robots to operate reliably in the real world, tackling perception, state estimation, modelling, action generation, planning, manipulation, and interaction with the world. The programme will teach students methods for robot perception, modelling, action generation, planning, and interaction with the world to act smartly and autonomously. It will combine competencies from engineering and computer science to build such systems and solve relevant problems of our society today – from sustainable technology and agricultural robotics to service robotic applications and autonomous vehicles. The programme links the engineering and computer science side of robotics with relevant societal applications, such as agricultural robotics and other fields such as autonomous cars. A key element is that such relevant applications operate at a high of excellence (supported, e.g. through PhenoRob). Furthermore, it tackles mobile systems, robots, and other vehicles that navigate and act autonomously without humans in the loop. The Mobile Robotics programme is extended over 24 months and is structured into mandatory and elective modules, including teaching units with a duration of one semester. Each module is completed with an exam. Three different types of modules are offered: lectures, seminars, and projects. The basis of evaluation is credit points (CP). The curriculum of the Mobile Robotics MSc programme is structured as follows: The modules of the first semester cover the basic knowledge required for the remaining programme. Lectures and exercises cover the following topics: Robot Planning and Control, GNSS, Inertial Navigation Systems, Python and Computer Vision, Graph SLAM, 3D Mapping and Machine Learning. In the second and third semester the students choose elective modules from the fields of geodesy and computer science. This gives the students a broad overview about the subject. It is also possible to choose a course from the university-wide offer with six credit points. Two project modules introduce the students to current research topics and train them to be able to do self-sufficient research. These topics are meant to be preparation for the Master's thesis. The last study semester is devoted to the Master's thesis.