Information Package / Course Catalogue
| Course Code | : ME431 |
| Course Type | : Area Elective |
| Couse Group | : First Cycle (Bachelor's Degree) |
| Education Language | : English |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 5 |
The aim of this course is to provide students with an integrated understanding of the fundamental components of robotic systems and the engineering applications of artificial intelligence methods. Students will examine perception, motion planning, control, machine learning, and autonomous decision-making concepts, and evaluate the design and operation of intelligent robotic systems.
This course covers robotics kinematics and dynamics, sensors, actuators, feedback control, mobile robots, computer vision, machine learning, deep learning, and autonomous system architectures within an interdisciplinary framework. The curriculum includes perception and sensor fusion, SLAM, path planning, human-robot interaction, and ethical/responsible AI. Students will explore current applications and evaluate robotics-AI integration through small-scale projects.
| 1. | Analyze the interdisciplinary structure of robotics and AI by explaining fundamental components, kinematics/dynamics, and data-driven decision processes |
| 2. | Design a basic robotic system by selecting appropriate sensors, actuators, control structures, and AI approaches for a given engineering problem. |
| 3. | Compare and select suitable algorithms for localization, path planning, or object detection in mobile robots or manipulators. |
| 4. | Evaluate machine learning and deep learning methods in robotic applications in terms of data quality, model performance, and computational cost |
| 5. | Critically assess current developments in robotics and AI considering ethics, safety, sustainability, and societal impact. |
| 1. | Siciliano, B. & Khatib, O., Springer Handbook of Robotics, 2nd Edition, Springer, 2016. |
| 2. | Russell, S. & Norvig, P., Artificial Intelligence: A Modern Approach, 4th Edition, Pearson, 2021. |
| 3. | Corke, P., Robotics, Vision and Control: Fundamental Algorithms in MATLAB, Springer, 2017. |
| Type of Assessment | Count | Percent |
|---|---|---|
| Assignment | 3 | %10 |
| Project | 1 | %20 |
| Midterm Examination | 1 | %20 |
| Final Examination | 1 | %50 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 1 | 3 | 56 |
| Assignment | 3 | 5 | 0 | 15 |
| Project | 1 | 10 | 0 | 10 |
| Reading | 14 | 1 | 0 | 14 |
| Midterm Examination | 1 | 9 | 1 | 10 |
| Final Examination | 1 | 18 | 2 | 20 |
| TOTAL WORKLOAD (hours) | 125 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | PÇ-12 | |
OÇ-1 | 5 | 4 | 3 | 4 | 3 | 5 | 5 | 3 | 3 | 3 | 2 | 3 |
OÇ-2 | 4 | 5 | 4 | 4 | 3 | 3 | 4 | 3 | 4 | 3 | 5 | 3 |
OÇ-3 | 4 | 5 | 3 | 4 | 3 | 5 | 5 | 4 | 4 | 3 | 4 | 4 |
OÇ-4 | 5 | 4 | 4 | 5 | 4 | 3 | 3 | 4 | 4 | 5 | 5 | 4 |
OÇ-5 | 4 | 5 | 4 | 3 | 3 | 3 | 4 | 3 | 4 | 5 | 3 | 5 |