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Abstract
This course is a hands-on introduction to self-driving cars using the Duckietown platform.Each student is given a mobile wheeled robot and throughout the class must configure and program.
Objective
This course covers the basics of modeling, perception, planning, control, and learning for autonomous systems. The focus is on learning the foundational elements of a robotics platform and understanding how these components integrate and interact. The objective of the class is to provide students with a practical understanding of what it takes to design and operate an autonomous mobile system, from a single unit up to a full fleet of robotic systems.
Content
Perception, planning, modeling, and control, leveraging primarily on vision data.
Resources
Lecture Notes
Lecture notes, primarily in the form of slides and tutorials, will be accessible from Moodle.Additional materials can also be accessed from the EdX MOOC called "Self-driving cars with Duckietown".
Literature
Course notes will be provided in an electronic form. These are some books that can be used to provide background information or consulted as references: (1) Siegwart, Nourbakhsh, Scaramuzza - Introduction to autonomous mobile robots; (2) Norvig, Russell - Artificial Intelligence, a modern approach. (3) Peter Corke - Robotics Vision and Control (4) Oussama Khatib, Bruno Siciliano - Handbook of Robotics
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Signup End
- 16.09.2026
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Hands-on Self-Driving Cars with Duckietown | No time listed | 4 h weekly |
Offered In
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Core Courses (The Core Courses in the Master’s program Mechanical Engineering listed below are indicative and include courses designed by the Department at the Master's level. With the approval of the tutor, students may also select Master's-level courses offered by other departments at ETH. These courses will be marked as non-regular in the LAG, but their categorization as Core Courses is possible if included in the approved LAG.)
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Deep Track Courses (At least 20 credits must be completed within the deep track courses. Surplus credit points can be counted towards the electives.)
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Deep Track Robotics (These courses can be credited either as a specialization subject or as an elective subject.)
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