This research-focused master’s trains students to design and build intelligent mobile robots that perceive, model, navigate and act autonomously in real-world environments. You will learn the theoretical foundations and state-of-the-art methods needed for robots to understand their surroundings, estimate state, plan and execute actions, manipulate objects and interact with people and environments.
The curriculum emphasizes probabilistic and machine-learning approaches that make mobile systems robust outside laboratory conditions. Teaching and research bridge computer science and engineering, providing the technical skills to develop complete robotic systems and to tackle applied problems such as sustainable technologies, agricultural robotics, service robots and autonomous vehicles.
A distinguishing feature is the close link between core robotics methods and high-quality application projects (supported, for example, through PhenoRob). The programme concentrates on mobile systems and vehicles that navigate and operate without a human in the loop, preparing graduates for research roles or application-driven work in industry and academia.
Curriculum overview
This two‑year (24‑month) Master's programme combines compulsory and elective semester‑long modules, each assessed by an exam and quantified using credit points (CP). Teaching formats include lectures, seminars and project work, giving a mix of theoretical instruction and hands‑on tasks. Throughout the course students progressively build the foundations in the first semester, deepen specialist knowledge in the middle semesters, and focus on independent research in the final semester.
Core modules and skills
The first semester delivers the essential technical building blocks needed for advanced work in mobile robotics: Robot Planning and Control, GNSS, Inertial Navigation Systems, Python and Computer Vision, Graph SLAM, 3D Mapping and Machine Learning. These modules develop competencies in autonomous navigation, sensor‑based perception and algorithmic mapping, as well as practical programming skills. In semesters two and three, students choose electives from geodesy and computer science to broaden their perspective and tailor the programme to their interests; there is also the option to include a university‑wide course worth six CP.
Research preparation and thesis
Two project modules during the programme expose students to contemporary research questions and train them to conduct self‑directed research — explicitly preparing them for the Master’s thesis, which occupies the final semester. By the end of the programme students should be able to integrate planning, sensing and mapping methodologies, implement algorithms in Python, and undertake independent research work in mobile robotics topics.
Programme requirements (concise)
This master’s programme is designed for highly motivated applicants who hold a strong Bachelor’s degree in computer science, geodesy, or closely related fields such as electrical engineering, applied mathematics, or similar subjects. Successful candidates are expected to already have solid foundations in mathematics, programming (for example Python), and basic engineering skills.
Admission is governed by § 5 of the Examination Regulations for the Master’s Programme. Applicants must meet both the grade and coursework/credit requirements; the final assessment of eligibility during application will include a grade conversion using the modified Bavarian formula.
Before you apply, make sure you can document the required course credits and the thesis credit. If your degree uses a different grading scale or credit system, the conversion and evaluation will be carried out during the application process — check the current minimum grade and other details carefully as the minimum grade requirement may change.
Winter Semester (International)
31 May 2026
Winter Semester (EU/EEA)
15 August 2026
Graduates are prepared for technical and research-oriented careers in robotics and autonomous systems, including roles in R&D teams, perception and localization engineering, control and planning for autonomous vehicles, and robotics system integration. The programme’s strong research and project focus also provides a solid foundation for pursuing a PhD or academic research positions.
Alumni can work in industry sectors such as agricultural technology, automotive and mobility companies, service robotics, start-ups and technology consultancies, as well as in research institutes and labs that develop navigation, mapping and machine-learning solutions for mobile robots.
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