Subject description - BE3M33ARO1

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

Web page:

https://cw.fel.cvut.cz/wiki/courses/aro

Anotation:

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.
IMPORTANT: It is assumed that students of this course have a working knowledge of optimization (Gauss-Newton method, Levenberg Marquardt method, full Newton method), mathematical analysis (gradient, Jacobian, Hessian), linear algebra (least-squares method), probability theory (multivariate gaussian probability), statistics (maximum likelihood and maximum aposteriori estimate), python programming and machine learning algorithms. This course is also part of the inter-university programme prg.ai Minor. It pools the best of AI education in Prague to provide students with a deeper and broader insight into the field of artificial intelligence. More information is available at https://prg.ai/minor.

Content:

https://cw.fel.cvut.cz/b212/courses/aro/start

Course outlines:

https://cw.fel.cvut.cz/b212/courses/aro/lectures/start

Exercises outline:

https://cw.fel.cvut.cz/b212/courses/aro/tutorials/start

Literature:

1. Siciliano, Bruno and Sciavicco, Lorenzo and Villani, Luigi and Oriolo, Giuseppe: Robotics, Modelling,
Planning and Control, Springer 2009
2. Fahimi, F.: Autonomous Robots: Modeling, Path Planning, and Control, Springer 2009

Requirements:

It is assumed that students of this course have a working knowledge of optimization (Gauss-Newton method, Levenberg Marquardt method, full Newton method), mathematical analysis (gradient, Jacobian, Hessian, multidimensional Taylor polynomial), linear algebra (least-squares method), probability theory (multivariate gaussian probability), statistics (maximum likelihood and maximum aposteriori estimate), python programming and machine learning algorithms.

Subject is included into these academic programs:

Program Branch Role Recommended semester
MEKYR_2021 Common courses P 2
MELAK_2016 Avionics PV 2


Page updated 19.5.2024 15:51:18, semester: Z/2024-5, Z,L/2023-4, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)