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: | - |
