MCE 484 - Aerial Robotics
Undergraduate course, AYBÜ, Department of Mechanical Engineering, 2025
This course introduces students to the theoretical foundations and practical implementation of aerial robotic systems, particularly Vertical Take-Off and Landing (VTOL) platforms such as quadrotors. Topics include modeling of aerial vehicles, 3D state and rotation representations, control techniques, motion planning algorithms, and state estimation methods using probabilistic filters.
Course Objectives
The course aims to:
- Provide mathematical modeling tools for aerial robots.
- Develop control techniques suitable for aerial platforms.
- Teach planning and estimation algorithms used in autonomous aerial robotics.
- Equip students with the ability to implement simulation-based and/or lab-based control systems for drones.
Learning Outcomes
At the end of the semester, students will be able to:
- Derive the equations of motion for multirotor aerial vehicles.
- Represent robot state in both 2D and 3D coordinate frames.
- Apply linear and nonlinear control strategies.
- Plan paths and trajectories using algorithms such as A*, RRT.
- Use state estimation techniques such as Kalman filters and particle filters.
- Demonstrate control of a simulated or physical drone in a controlled setting.
Weekly Topics
| Week(s) | Topics |
|---|---|
| 1–2 | Position and rotation representations, rigid body transformations, angular velocity |
| 3–4 | Quadrotor dynamics, modeling platforms with payloads, state-space representation |
| 5–6 | Linear control, nonlinear control, differential flatness |
| 7–8 | Position and trajectory controllers, time-parameterized paths |
| 9–11 | Path and motion planning: A*, graph search, RRT |
| 12–14 | Bayesian filters: KF, EKF, UKF, PF, Monte Carlo localization |
Reference Materials
- Thrun, S. Probabilistic Robotics, Communications of the ACM, 2002.
- Choset, H. M., et al. Principles of Robot Motion, MIT Press, 2005.
- Corke, P. Robotics, Vision and Control, Springer, 2017.
- Särkkä, S. Bayesian Filtering and Smoothing, Cambridge University Press, 2013.