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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2024/2025

Autonomous Robotics

The course is not on the list Without time-table
Code Completion Credits Range Language
BE3M33ARO1 Z,ZK 6 2P+2L English
Vztahy:
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:
Lecturer:
Tutor:
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
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2024-05-25
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet6653706.html