• Links to discussion forum, BRUTE
• schedule: B3M33ARO BE3M33ARO
• Rules for independent work
• How to Turtlebots

In order to minimize the risk of spreading the coronavirus, contact (classroom) teaching has been replaced by web-based distance learning, see regularly updated list of coronavirus news for more details.

• Lectures are recorded offline, students are informed about the availability of a new lecture by email. PDF worksheets with examples to be solved by students are provided with each particular lecture. Both the lectures and the examples from the worksheet are expected to be consulted via discussion forum. See lectures webpage for more details.
• Labs are provided in the form of offline tutorials. All of the labs are either accompanied by an accurate list of instructions or online discussions in the time slot of the labs. Students will be informed about the form of each particular lab not later than the beginning of the corresponding week. See labs webpage for more details.
• Semestral work is scheduled for the second half of the term. Since the work with the real robots seems unlikely due to the quarantine, we are recently working on a suitable replacement. Students will be informed about the replacement not later than 20.4. (postponed due to Easter holidays + shift in Thursday labs)

The Autonomous robotics course will explain the principles needed to develop algorithms for mobile robots. In particular, the main focus is on:

• Mapping the world (e.g. 3D semantic map) from measurements of exteroceptive sensors such as camera or lidar
  (see lectures 3,4,5,6,7 on calibration, SLAM and deep learning on images).
• Planning in the map (see lectures 1,2,9 on planning).
• Performing the plan in the world (lectures 8,11,12,13 on reinforcement learning and motion control).

It is assumed that students of this course have a working knowledge of mathematical analysis, linear algebra, probability theory, and statistics. In addition, basic programming skills in python are expected.

The course consists of lectures and labs, both detailed in the following links:

• Lectures takes place on Monday from 10:00 in KN:E107 see the schedule for details.
• Labs takes place in KN:E-123. The first 7 weeks will be focused on theoretical labs and homework, the remaining 7 weeks are devoted to solving the semestral work in RoboLab KN:E-130.

The consultation will be provided on an email request.

Maximum number of points is 100. Points are structured as follows:

• homework (4×7 = 28),
• semestral work (22),
• midterm test (15),
• exam test (35).

Minimum credit requirements:

• Upload own solution of the semestral work before the beginning of the labs in the thirteenth week.
• Show own solution of all homework before the beginning of the labs in the thirteenth week.
• Achieve at least one point from each homework (without considering a penalty for the late upload), semestral work, and midterm test.

The final grade will be determined by the total number of points according to the following table

1. [KZ] Goodfellow et al. Deep Learning, 2016 http://www.deeplearningbook.org
2. [KZ] Hartley, Zisserman Multipleview Geometry, 2004, https://www.robots.ox.ac.uk/~vgg/hzbook
3. [VV] Steven M. LaValle. Planning Algorithms, Cambridge University Press, 2006. (volně na internetu, http://planning.cs.uiuc.edu/)
4. [VH] B. Siciliano, O. Khatib (editoři). Handbook of Robotics, Springer-Verlag, Berlin 2008.

We want students to work individually, therefore any plagiarism in codes, homework or reports will be mercilessly punished . We strongly urge each student to read what is/isnot a plagiarism - we believe that many students will be surprised. In any case, it is not permitted to use the work of your colleagues or predecessors. Each student is responsible for ensuring that his work does not get into the hands of other colleagues. In the case of multiple submission of the same work, all involved students will be penalized, including those who gave the work available to others