Information Package / Course Catalogue
Aerial Robotics
Course Code: RYZ124
Course Type: Area Elective
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 3
Objectives of the Course

To equip students with the ability to model and control an unmanned aerial vehicle and to develop a mission-oriented aerial robot by integrating autonomous flight with AI-based perception and decision capabilities.

Course Content

This course addresses the design, modeling, control and autonomous flight of unmanned aerial vehicles (UAV/drone), blending them with artificial intelligence methods. The aerodynamics and dynamic model of multirotor and fixed-wing platforms, flight control systems, sensor fusion and state estimation, autonomous navigation, trajectory planning, vision-based perception and learning-based (reinforcement learning) control are studied in an applied manner.

Name of Lecturer(s)
Learning Outcomes
1.Explains and builds the dynamic model of multirotor and fixed-wing platforms.
2.Theoretically designs and analyzes the flight control system (attitude, altitude, position).
3.Explains the principles of sensor fusion, state estimation and autonomous navigation.
4.Explains and compares trajectory planning and obstacle-avoidance algorithms.
5.Evaluates vision-based perception and learning-based control approaches in the aerial-vehicle context.
Recommended or Required Reading
1.Aerial Robotic Manipulation: Research, Development and Applications (Springer Tracts in Advanced Robotics Book 129) by Anibal Ollero (Editor), Bruno Siciliano (Editor)
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to aerial robotics; UAV types and application areas
Week 2 - Theoretical
Fundamentals of aerodynamics and propulsion systems
Week 3 - Theoretical
Multirotor dynamic model and reference frames
Week 4 - Theoretical
Modeling of fixed-wing and VTOL platforms
Week 5 - Theoretical
Flight control: attitude and altitude control (PID/cascaded)
Week 6 - Theoretical
Position control and flight controllers (autopilot)
Week 7 - Theoretical
Sensors and sensor fusion (IMU/GPS/barometer); state estimation (Kalman)
Week 8 - Theoretical
Autonomous flight architecture and mission planning
Week 9 - Theoretical
Trajectory planning and obstacle avoidance
Week 10 - Theoretical
Vision-based navigation: optical flow, visual odometry and SLAM
Week 11 - Theoretical
AI-based perception: deep-learning object detection and tracking
Week 12 - Theoretical
Learning-based (reinforcement learning) flight control and sim-to-real
Week 13 - Theoretical
Case studies: autonomous mission scenarios
Week 14 - Theoretical
Case studies: autonomous mission scenarios
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures10%5
Quiz1%5
Midterm Examination1%30
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Assignment30515
Presentation 3026
Individual Work100220
Quiz1011
Midterm Examination1011
Final Examination1011
TOTAL WORKLOAD (hours)72
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
PÇ-14
PÇ-15
OÇ-1
5
5
5
5
5
5
5
OÇ-2
5
5
5
5
5
5
5
OÇ-3
5
5
5
5
5
5
5
OÇ-4
5
5
5
5
5
5
5
OÇ-5
5
5
5
5
5
5
5
Adnan Menderes University - Information Package / Course Catalogue
2026