After completing this course, the student will have a broad overview of existing AI-based methods for mobile robot navigation and controlknow advantages and disadvantages of various methods depending on the task at handhave gained hands-on experience implementing state-of-the art AI methods for solving problems in mobile robot navigation

The following teaching methods will be used:presentation of novel content with examplesgroup excercises during class and discussionhome excercises / mini-projectsThe mini-projects are realistic use cases in mobile robot navigation that shall be solved using the AI methods discussed in the course and presented to the class during dedicated sessions in the second half of the semester. 

Overview of AI methods with focus on roboticsAI methods for state estimationAI-based control of mobile robotsDepending on time and interest, other topics may be included such as simulation frameworks

Grading is based on the active participation in class (presentation of group excercises, active participation in group excercises, active contributions to discussions etc.), and the successful conclusion and presentation of mini-projects assigned during class. 

All contents covered in the course.

The grades will be determined by active participation during class (50%)successful conclusion and presentation of mini-projects (50%)The final grade will be the weighted average of the two grades. Please note that this course requires regular attendance. An unexcused absence from more than 5 classes will automatically lead to a failing grade for the entire course. Dropping out is possible until the end of November.