Robotics
People
Course director
Description
The course theory part explores the following topics:
- 2D and 3D pose representation and transformations
- Workspace, C-Space, Degrees of Freedom
- Kinematics for arms and wheeled robots
- Feedback-based control
- Sensors
- Localization, Mapping, SLAM
- Path planning
The practical part explores algorithm implementations with Jupyter Notebooks and ROS.
Good Python programming skills are required for assignments and project work.
Objectives
- Learn the fundamental problems, and their standard solutions, in autonomous mobile robotics and arm robotics.
- Gain hands-on experience in implementing algorithms for mobile robots using Python.
- Learn to use ROS, a powerful software ecosystem for robotics.
Sustainable development goals
- Decent work and economic growth
- Industry, innovation and infrastructure
Teaching mode
In presence
Learning methods
- Implementing algorithms and solving exercises
- Studying theory topics
- Hands-on projects
Examination information
- 50% theory (written exam)
- 50% labs and assignments
Extra credit will be assigned with short in-class written theory questions during the semester.
Bibliography
Deepening
- Ben-Ari, Mordechai, Mondada, Francesco. Elements of Robotics. Springer Open, 2017. (https://link.springer.com/book/10.1007/978-3-319-62533-1)
- Siegwart, Roland, Nourbakhsh, Illah Reza, Scaramuzza, Davide. Introduction to autonomous mobile robots. 2nd ed.. Cambridge, MA: MIT Press, 2011. (http://www.mobilerobots.ethz.ch/)
Education
- Master of Science in Artificial Intelligence, Lecture and Laboratory, 1st year
- Master of Science in Computational Science, Lecture and Laboratory, Elective, 2nd year
- Master of Science in Financial Technology and Computing, Lecture and Laboratory, Elective, 1st year
- Master of Science in Financial Technology and Computing, Lecture and Laboratory, Elective, 2nd year
- Master of Science in Informatics, Lecture and Laboratory, Artificial Intelligence, Elective, 1st year
- Master of Science in Informatics, Lecture and Laboratory, Artificial Intelligence, Elective, 2nd year
- Master of Science in Informatics, Lecture and Laboratory, Geometric and Visual Computing, Elective, 1st year
- Master of Science in Informatics, Lecture and Laboratory, Geometric and Visual Computing, Elective, 2nd year
- Master of Science in Management and Informatics, Lecture and Laboratory, Elective, 1st year
- Master of Science in Management and Informatics, Lecture and Laboratory, Elective, 2nd year
- PhD programme of the Faculty of Informatics, Lecture and Laboratory, Elective, 1st year (4.0 ECTS)
