Teacher: Konstantinos Alexis

The course will relate to the problem of Resilient Autonomy for Aerial Robots. Topics will cover:

• T1: Modeling and Control of Micro Aerial Vehicles
• T2: State Estimation & Localization And Mapping
• T3: Path Planning for Collision-free Flight and Extrinsic Objective Optimization

We will follow a breadth-first approach followed by deep-dive on one particular topic. Accordingly, all topics mentioned above will be quickly overviewed in the first phase of the class (5 lectures) followed by a deep dive specifically on State Estimation using GPS/IMU fusion and GPS-denied Localization And Mapping for Flying Robots.

In the course, there will be a semester-long project with research focus in which you would work as teams. There will also be coding assignments relating to robot control, state estimation and path planning. Coding will be in C++ and Python.

Some of the material will rely on the following previous class: https://www.autonomousrobotslab.com/introduction-to-aerial-robotics.html

However you should expect significant changes as this class was for 3rd year undergraduate students in Computer Sciene (in a different university) and thus of lower-complexity compared to what this specialization course will be. This relates especially to the domain of «deep dive» where we will have multiple lectures on advanced topics of nonlinear state estimation for autonomous flying robots.

Lecture slides, assignments and other details will be posted at: https://www.autonomousrobotslab.com/aerial-robotic-autonomy.html

During Fall 2022, the class meets on Thursdays from 10:15-12 at MA24.