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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024
UPOZORNĚNÍ: Jsou dostupné studijní plány pro následující akademický rok.

Autonomous Robotics

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Code Completion Credits Range Language
BE3M33ARO1 Z,ZK 6 2P+2L English

During a review of study plans, the course B3M33ARO1 can be substituted for the course BE3M33ARO1.

It is not possible to register for the course BE3M33ARO1 if the student is concurrently registered for or has already completed the course B3M33ARO1 (mutually exclusive courses).

It is not possible to register for the course BE3M33ARO1 if the student is concurrently registered for or has already completed the course BE3M33ARO (mutually exclusive courses).

It is not possible to register for the course BE3M33ARO1 if the student is concurrently registered for or has already completed the course B3M33ARO (mutually exclusive courses).

During a review of study plans, the course BE3M33ARO can be substituted for the course BE3M33ARO1.

During a review of study plans, the course B3M33ARO can be substituted for the course BE3M33ARO1.

It is not possible to register for the course BE3M33ARO1 if the student is concurrently registered for or has previously completed the course B3M33ARO1 (mutually exclusive courses).

It is not possible to register for the course BE3M33ARO1 if the student is concurrently registered for or has previously completed the course BE3M33ARO (mutually exclusive courses).

It is not possible to register for the course BE3M33ARO1 if the student is concurrently registered for or has previously completed the course B3M33ARO (mutually exclusive courses).

Garant předmětu:
Karel Zimmermann
Lecturer:
Vojtěch Vonásek, Karel Zimmermann
Tutor:
Bedřich Himmel, Vít Krátký, František Nekovář, Martin Pecka, Robert Pěnička, Vojtěch Vonásek, Karel Zimmermann
Supervisor:
Department of Cybernetics
Synopsis:

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.

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.

Syllabus of lectures:

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

Syllabus of tutorials:

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

Study Objective:
Study materials:

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

Note:
Further information:
https://cw.fel.cvut.cz/wiki/courses/aro
Time-table for winter semester 2023/2024:
Time-table is not available yet
Time-table for summer semester 2023/2024:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
roomKN:E-107
Zimmermann K.
Vonásek V.

11:00–12:30
(lecture parallel1)
Karlovo nám.
Zengerova posluchárna K1
roomKN:E-132
Pěnička R.
Pecka M.

14:30–16:00
(lecture parallel1
parallel nr.101)

Karlovo nám.
Laboratoř PC
Tue
Wed
Thu
Fri
The course is a part of the following study plans:
Data valid to 2024-03-27
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet6653706.html