Subject description - BE3M33ARO1

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BE3M33ARO1 Autonomous Robotics
Roles:P Extent of teaching:2P+2L
Department:13133 Language of teaching:EN
Guarantors:Zimmermann K. Completion:Z,ZK
Lecturers:Hlaváč V., Vonásek V., Zimmermann K. Credits:6
Tutors:Too many persons Semester:L


The Autonomous robotics course will explain the principles needed to develop algorithms for intelligent mobile robots such as algorithms for:
(1) Mapping and localization (SLAM) sensors calibration (lidar or camera).
(2) Planning the path in the existing map or planning the exploration in a partially unknown map and performing the plan in the world.
It is assumed that students of this course have a working knowledge of mathematical analysis, linear algebra, probability theory, statistics, python programming and machine learning algorithms.


Course outlines:

Exercises outline:

Exercises: laboratory tasks with autonomous robots (with robots from research projects, with the iRobot Create building set). Students will acquire datasets using robots, will solve three tasks and will demonstrate them on the robots.


1. Goodfellow et al. Deep Learning, 2016,
2. Hartley, Zisserman Multipleview Geometry, 2004,
3. Steven M. LaValle. Planning Algorithms, Cambridge University Press, 2006,
4. B. Siciliano, O. Khatib. Handbook of Robotics, Springer-Verlag, Berlin 2008.




autonomous robotics

Subject is included into these academic programs:

Program Branch Role Recommended semester
MEKYR_2021 Common courses P 2

Page updated 1.10.2022 05:51:57, semester: Z,L/2022-3, L/2021-2, Z/2024-5, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)