Autonomous Robotics

Relations:

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

It is not possible to register for the course B3M33ARO1 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 B3M33ARO can be substituted for the course B3M33ARO1.

The requirement for course B3M33ARO1 can be fulfilled by substitution with the course BE3M33ARO1.

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

It is not possible to register for the course B3M33ARO1 if the student is concurrently registered for or has previously completed the course B3M33ARO (mutually exclusive courses).

Course guarantor:

Karel Zimmermann

Lecturer:

Vojtěch Vonásek, Karel Zimmermann

Tutor:

Bedřich Himmel, Vít Krátký, Tomáš Musil, David Pařil, Martin Pecka, 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/wiki/courses/aro/lectures/start

Syllabus of tutorials:

https://cw.fel.cvut.cz/wiki/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 2024/2025:

Time-table is not available yet

Time-table for summer semester 2024/2025:

	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.
Tue
Wed
Thu	roomKN:E-132 Pařil D. 09:15–10:45 (lecture parallel1 parallel nr.101) Karlovo nám. roomKN:E-132 Krátký V. 11:00–12:30 (lecture parallel1 parallel nr.102) Karlovo nám. roomKN:E-132 Musil T. 12:45–14:15 (lecture parallel1 parallel nr.103) Karlovo nám.
Fri

The course is a part of the following study plans:

• Aerospace Engineering (compulsory elective course)
• Cybernetics and Robotics (compulsory course in the program)