Subject

Autonomous robotics

Course Title

Autonomous robotics
Autonomous robotics

Code

F23L3W072

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First Cycle

Academic year / semester

7 / Winter

Number of ECTS credits

Teacher

Андреа Кулаков, Невена Ацковска

Prerequisites for enrolling in the course

Основи на роботика или Алгоритми и податочни структури

10.

Objectives of the course programme (competences)

Upon completion of this course, the student is expected to have in-depth knowledge of the development of autonomous robotic systems (autonomous vehicles, autonomous aircraft, etc.) using probabilistic approaches in robotics.

11.

Course content

Lectures:
1. Сензори и ефектори во автономната роботика
2. Mатематички принципи во автономната роботика
3. Веројатностни модели на сензорите
4. Веројатностни модели на управувањето
5. Калманов филтер и негови имплементации
6. Калманов филтер и негови имплементации
7. Калманов филтер и негови имплементации
8. Мапирање
9. Локализација
10. Истовремено мапирање и локализација
11. Истовремено мапирање и локализација
12. Планирање и учење кај интелигентните роботски системи
13. Планирање и учење кај интелигентните роботски системи

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defense of a project assignment, learning in an electronic environment (forums, consultations).

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	45 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	75 hours

17.

Assessment method

17.1.	Tests	10 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	10 points
17.4.	Final exam	70 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

Regular attendance in class, timely submission of homework, and presentation of the project assignment.

20.

Language of instruction

Macedonian

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature 1. Sebastian Thurn, Wolfram Burgard, Dieter Fox \| Probabilistic Robotics \| MIT Press \| 2005 2. Roland Siegwart, Illah Reza Nourbakhsh, Davide Scaramuzza \| Introduction to Autonomous Mobile Robots \| MIT Press \| 2011
22.2.	Additional literature No. Author Title Publisher Year