Faculty: Faculty of Engineering

Department(s): All faculty (CIV, CE, IE, EE, IM)

Number of Students:

Course: Introduction to Robotics

Weekly hours: Theory: 2 Exercises: 0

ECTS Credits: 4

Semester: Fall

Lecture Schedules:

Classroom: B-306 (computer lab) - temporary for any changes you will be informed in the classroom.

Day/time: Friday; 9:15AM - 11:00AM

Lecturer: Dr. Hiqmet Kamberaj

Room Number: —-

Phone Number of the lecturer:

E-mail address of the lecturer: km.ude.ubi|jarebmakh#km.ude.ubi|jarebmakh

**Course Objectives:**

In this course, we aim to give an introduction to the elements of the robotics. The students will be able to learn about the robotics and their applications; Sensors; Reactive behavior; Finite state machines; Robotic motion and odometry; Control; Local navigations; Localization; Mapping and Mapping-based navigation; Fuzzy logic control; Image processing; Neural networks; Machine learning; Swarm robotics; Kinematics of a robotic manipulator.

**Learning Outcomes:**

Skill outcomes | Necessary ( + ) Not Necessary ( –) |
---|---|

Written communication skills | + |

Oral communication skills | + |

Computer skills | + |

Working in laboratory | + |

Working team | + |

Preparing projects | + |

Knowledge of foreign language (English) | + |

Scientific and professional literature analysis | + |

Problem solving skills | + |

Management skills | + |

Presentation skills | + |

**Course Textbooks:**

- H. Kamberaj, Lecture Notes in Robotics, 2022.

**Teaching methods:**

Teaching methods | Ideal % |
---|---|

Teaching ex cathedra (teacher as the figure of authority, standing in front of the class and lecturing) | 70 |

Interactive teaching (ask questions in class, assign and check homework, or hold class or group discussions) | 20 |

Mentor teaching (consultant-teacher who has a supervisory responsibility and supervising the students) | - |

Laboratory work | - |

Seminar work | 5 |

Field Work (enables students to examine the theories and the practical experiences of a particular discipline interact) | - |

Semester project | - |

Case Study (An in-depth exploration of a particular context) | - |

Students Teamwork | 5 |

**Attendance:**

- Students are obliged to attend at least 72 % out of 12 weeks of lectures, exercises, and other activities.
- The attendance rule for failed overlapping courses is 36 % out of 12 weeks of lectures, exercises, and other activities.
- The attendance rule for course from the upper semester is 57% out of 12 weeks of lectures, exercises, and other activities.
- Students are not obliged to attend the course if the course is double repeated. However, they need to register the course.

**Exams (Mid-Term Exam, Final Exam, Make-up Exam):**

There are two exams, the Mid-Term and Final Exam, at the middle and at the end of the semester, respectively. The students, who do not earn minimum 50 credit points from the Mid-Term, Final Exam including Homework Assignments, have to take the Make-Up Exam, which counts only for Final Exam credit points. The terms of the exams are defined by the Academic Calendar announced on the University web site.

**Passing Score:**

The maximum number of credit points is collected during the semester, as follows: Mid-term Exam = 40 Credit Points (minimum requirement is 25 % (midterm exam + activity) to enter Final Exam), Final Exam (minimum requirement is 25 % to pass) = 40 Credit Points. Homeworks, quizzes, specific assignments and term papers = 20 Credit Points. Total=100.

**Study Plan** —- International Balkan University - Academic Calendar

~Week | Lecture | ~ Topics |
---|---|---|

1 | 1 | Introduction to the philosophy of this course. Robotics and their Applications. |

2 | 2 | Sensors. Reactive behavior. |

3 | 3 | Finite state machines. Robotic Motion and Odometry. |

4 | 4 | Control. Local Navigation and Localization. |

5 | 5 | Mapping and Mapping-based Navigation. |

6 | 6 | Midterm review. |

7 | - | Mid Term Exam Week |

8 | 7 | Fuzzy logic control. |

9 | 8 | Image processing. |

10 | 9 | Neural Networks and Machine Learning. |

11 | 10 | Swarm robotics. |

12 | 11 | Kinematics and a robotic manipulator. |

13 | 12 | Final exam review. |

14 | - | Winter break. |

15 | - | Final exam week. |

16 | - | Preparatory week. |

17 | - | Make up exam week. |

**Student workload:**

For calculating the Total Student Work Load we multiply the course ECTS credits with standard figure 30. (ECTS Credit: 4) x 30 = 120 hours.

Activities | Hours |
---|---|

Lecture hours for 12 weeks: | 24 |

Laboratory and class exercises for 12 weeks: | - |

Student Mentoring for 12 weeks: | 10 |

Consultation for 12 weeks: | 10 |

Exam preparations and exam hours (Midterm, final, Makeups): | 36 |

Individual reading work for 12 weeks (Reading assignments/expectations for reading and comprehension is 5 pages per hour. Example: If a book 300 pages, total Individual reading work for 12weeks 300:5 = 60 hours. | 20 |

Homework and work practice for 12 weeks: | 20 |

Preparation of diploma work, for 12 weeks: | - |