Autonomous robotics – B(E)3M33ARO

• Links to discussion forum, BRUTE
• Timetable: B3M33ARO/BE3M33ARO
• Rules for independent work

It is assumed that students of this course have a working knowledge of

• mathematical analysis,
• optimization,
• linear algebra,
• probability theory,
• statistics,
• programming skills in python.

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

• Mapping and localization the world (e.g. pointcloud map, occupancy grid) from measurements of exteroceptive sensors such as camera or lidar
  (see lectures on sensor calibration, SLAM and deep learning on images).
• Planning in the map (see lectures Planning I-IV).
• Performing the plan in the world (Planning II and Reinforcement Learning).

The course consists of lectures and labs. Lectures take place in KN:E-107 every Monday at 11:00. Labs take place in KN:E-132, the time slot corresponds to the code of your course.

• Lectures: Some lecture are accompanied by a PDF worksheet with numerical problems to be solved by students (we do not check if students do so, however the individual solution of provided problems is highly recommended in order to pass the exam test).
• Labs: The first 8 weeks will be focused on regular labs, the second half is devoted to solving the semestral work. Most of the regular labs contain homework to be solved before the start of the following labs.
• Semestral work is scheduled for the second half of the term. Students will start to solve the semestral work in the Gazebo simulator. In case that the epidemiological conditions allow the physical presence of students in RoboLab (KN:E-130), students will be given the opportunity to demonstrate their solution on the real robot. Successful demonstration will be rewarded with bonus points. The consultation of semestral work will be provided during the consultation labs in the second half of the semester. In case of any problems which need to be solved immediately, please contact Frantisek Nekovar.

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

• homework (5x5p + 1x10p = 35),
• semestral work (10 simulation + 10 practical),
• exam test (45).

Minimum credit requirements:

• Upload own solution of all homework(which satisfy minimum requirements) before the beginning of the labs in the thirteenth week.
• Upload own solution of the semestral work for simulation evaluation
• Demonstration of the semestral work (it should explore at least 50% of the map when deployed on real robots)
• Active participation on all regular labs. Active participation means that students are able to demonstrate progress on the lab assignment and answer questions of the lab tutor.

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

1. [KZ] Thrun et al. Probabilistic robotics, MIT press 2017,[pdf]
2. [KZ] Hartley, Zisserman Multipleview Geometry, 2004, https://www.robots.ox.ac.uk/~vgg/hzbook
3. [KZ] Goodfellow et al. Deep Learning, 2016 http://www.deeplearningbook.org[pdf]
4. [VV] Steven M. LaValle. Planning Algorithms, Cambridge University Press, 2006. (volně na internetu, http://planning.cs.uiuc.edu/)
5. [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